{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "036fad56",
   "metadata": {},
   "outputs": [],
   "source": [
    "from westat import *\n",
    "\n",
    "# 导入 UCI_Credit_Card\n",
    "data = uci_credit_card()\n",
    "\n",
    "# 将目标变量重命名为“y”\n",
    "data.rename(columns={'target':'y'},inplace=True)\n",
    "\n",
    "# 使用 data_split 函数，按随机划分25%的数据到测试集进行数据分区\n",
    "data_train,data_test = data_split(data,0.25)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f438027d",
   "metadata": {},
   "source": [
    "## 特征分箱 binning"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1ee3d9b0",
   "metadata": {},
   "source": [
    "特征分箱（Binning）作为数据预处理的一部分，也被称为离散分箱或数据分段。分箱把数据根据一定的规则进行分组，使数据变得离散化，以增强模型的稳定性并避免过拟合。\n",
    "在评分卡建模中，变量分箱（binning）是对连续变量离散化（discretization）的一种称呼。要将logistic模型转换为标准评分卡的形式，这一环节是必须完成的。信用评分卡开发中一般有常用的等距分段、等深分段、最优分段。\n",
    "\n",
    "- 等距分段（Equval length intervals）：是指分段的区间是一致的，比如年龄以十年作为一个分段；\n",
    "- 等深分段（Equal frequency intervals）：是先确定分段数量，然后令每个分段中数据数量大致相等；\n",
    "- 最优分段（Optimal Binning）：又叫监督离散化（supervised discretizaion），使用递归划分（Recursive Partitioning）将连续变量分为分段，背后是一种基于条件推断查找较佳分组的算法（Conditional Inference Tree）。\n",
    "\n",
    "在实际建模中，分箱一般都是针对连续型数据（如价格、销量、年龄）进行的。但是从理论上，分箱也可以对分类型数据进行。\n",
    "\n",
    "所以经过分箱后的数据，有以下两个特点：\n",
    "- 数据可取值的范围变小了\n",
    "- 数据的可取值会更加确定与稳定\n",
    "- 数据中信息会变得模糊，不再那么精确\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fc60b668",
   "metadata": {},
   "source": [
    "**证据权重 WOE**\n",
    "\n",
    "全称为 weight of Evidence。WOE表示的是“当前分组中响应客户占所有响应客户的比例”和“当前分组中没有响应的客户占所有没有响应的客户的比例”的差异。也可以理解为当前分组中响应的客户和未响应客户的比值，和所有样本中这个比值的差异。\n",
    "\n",
    "**WOE 计算公式为：**\n",
    "\n",
    "$ WOE \\;=\\;ln\\left[\\frac{Bad\\;Distribution}{Good\\;Distribution}\\right] $\n",
    "\n",
    "如果括号内的比值小于1，WOE是负值；反之则是正值。需要注意的是，公式中将违约的分布作为分子只是一种选择，也可以将正常的分布作为分子来定义 WOE。公式中类别顺序的选择隐含着WOE的含义。\n",
    "\n",
    "在计算WOE之前，需要将变量分段（(bin)\n",
    "\n",
    "对于一个名义变量的类别i，或连续变量的某个段，WOE可以定义为:\n",
    "\n",
    "$ WOE_i \\;=\\;ln\\left[\\frac{Bad\\;Distribution_i}{Good\\;Distribution_i}\\right] $\n",
    "\n",
    "需要记住的是,对于类别i，正常和违约的分布分别定义为:\n",
    "\n",
    "$ Good\\,Distribution\\,_i\\;=\\; \\frac{Number\\,of\\,Good_i}{Total \\,Number\\,of\\,Good} $\n",
    "\n",
    "$ Bad\\,Distribution\\,_i\\;=\\; \\frac{Number\\,of\\,Bad_i}{Total \\,Number\\,of\\,Bad} $\n",
    "\n",
    "$ WOE_i\\;=\\;ln \\left( \\frac {\\#B_i / \\#B_T}{\\#G_i / \\#G_T} \\right) $\n",
    "\n",
    " **WOE的特点：**\n",
    "- 当前分组中，响应的比例越大，WOE值越大；\n",
    "- 当前分组WOE的正负，由当前分组响应和未响应的比例，与样本整体响应和未响应的比例的大小关系决定，当前分组的比例小于样本整体比例时，WOE为负，当前分组的比例大于整体比例时，WOE为正，当前分组的比例和整体比例相等时，WOE为0。\n",
    "- WOE的取值范围是全体实数。\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1ca9be6c",
   "metadata": {},
   "source": [
    "**信息价值 IV**\n",
    "\n",
    "全称为 information value，用于衡量某个变量的信息量。\n",
    "\n",
    "对于一个名义变量的类别i，或连续变量的某个段，IV可以定义为:：\n",
    "\n",
    "$ IV_i = \\left(\\frac{\\#B_i}{\\#B_T} - \\frac{\\#G_i}{\\#G_T}  \\right) * ln \\left( \\frac{\\#B_i/\\#B_T}{\\#G_i/\\#G_T} \\right) $\n",
    "\n",
    "最终变量的IV需要将每个分组的IV值进行加总，公式为：\n",
    "\n",
    "$ IV = \\sum^n_{k=0} IV_i $\n",
    "\n",
    "\n",
    "IV 解释能力\n",
    "\n",
    "| IV值 | 解释能力 |\n",
    "|-------|-------|\n",
    "| < 0.02 | 无 |\n",
    "| 0.02 - 0.1 | 低 |\n",
    "| 0.1 - 0.3 | 中 |\n",
    "| 0.3 - 0.5 | 高 |\n",
    "| > 0.5 | 太高，可能有问题 |\n",
    "\n",
    "\n",
    "**IV 的特点：**\n",
    "- 对于变量的一个分组，这个分组的响应和未响应的比例与样本整体响应和未响应的比例相差越大，IV值越大，否则，IV值越小；\n",
    "- 极端情况下，当前分组的响应和未响应的比例和样本整体的响应和未响应的比例相等时，IV值为0；\n",
    "- IV值的取值范围是[0,+∞)，且当当前分组中只包含响应客户或者未响应客户时，IV = +∞。\n",
    "- IV无论等于负无穷还是正无穷，都是没有意义的。\n",
    "IV的缺点，就是不能自动处理变量的分组中出现响应比例为0或100%的情况。遇到响应比例为0或者100%的情况，建议如下：\n",
    "1. 如果可能，直接把这个分组做成一个规则，作为模型的前置条件或补充条件；\n",
    "2. 重新对变量进行离散化或分组，使每个分组的响应比例都不为0且不为100%，尤其是当一个分组个体数很小时（比如小于100个），强烈建议这样做，因为本身把一个分组个体数弄得很小就不是太合理。\n",
    "3. 如果上面两种方法都无法使用，建议人工把该分组的响应数和非响应的数量进行一定的调整。如果响应数原本为0，可以人工调整响应数为1，如果非响应数原本为0，可以人工调整非响应数为1.\n",
    "\n",
    "\n",
    "**在逻辑回归中筛选变量，一般会考虑下面因素**\n",
    "- Magnitude of Information Value. Variables with higher Information Values are preferred;\n",
    "- Linearity. Variables exhibiting a strong linearity are preferred;\n",
    "- Coverage. Variables with fewer missing values are preferred;\n",
    "- Distribution. Variables with less uneven distributions are preferred;\n",
    "- Interpretation. Variables that make more sense in business are preferred."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1d740ecb",
   "metadata": {},
   "source": [
    "### 最优分箱 "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "68be91b6",
   "metadata": {},
   "source": [
    "westat默认使用最优分箱策略进行特征分箱，使用woe_iv函数，可以在分箱的同时进行WoE和IV值的计算，默认数据精度保留4位。\n",
    "在调用woe_iv函数时，至少需要传入目标数据集和需要进行分箱的特征变量名称"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "a5d80238",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>No.</th>\n",
       "      <th>Bin</th>\n",
       "      <th>#Total</th>\n",
       "      <th>#Bad</th>\n",
       "      <th>#Good</th>\n",
       "      <th>%Total</th>\n",
       "      <th>%Bad</th>\n",
       "      <th>%Good</th>\n",
       "      <th>%BadRate</th>\n",
       "      <th>WoE</th>\n",
       "      <th>IV</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AGE</td>\n",
       "      <td>1</td>\n",
       "      <td>(-inf, 25.5]</td>\n",
       "      <td>3871</td>\n",
       "      <td>1032</td>\n",
       "      <td>2839</td>\n",
       "      <td>12.9033%</td>\n",
       "      <td>15.5515%</td>\n",
       "      <td>12.1512%</td>\n",
       "      <td>26.6598%</td>\n",
       "      <td>0.2467</td>\n",
       "      <td>0.0084</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AGE</td>\n",
       "      <td>2</td>\n",
       "      <td>(25.5, 28.5]</td>\n",
       "      <td>4142</td>\n",
       "      <td>852</td>\n",
       "      <td>3290</td>\n",
       "      <td>13.8067%</td>\n",
       "      <td>12.8391%</td>\n",
       "      <td>14.0815%</td>\n",
       "      <td>20.5698%</td>\n",
       "      <td>-0.0924</td>\n",
       "      <td>0.0011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AGE</td>\n",
       "      <td>3</td>\n",
       "      <td>(28.5, 35.5]</td>\n",
       "      <td>8796</td>\n",
       "      <td>1713</td>\n",
       "      <td>7083</td>\n",
       "      <td>29.3200%</td>\n",
       "      <td>25.8137%</td>\n",
       "      <td>30.3159%</td>\n",
       "      <td>19.4748%</td>\n",
       "      <td>-0.1608</td>\n",
       "      <td>0.0072</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>AGE</td>\n",
       "      <td>4</td>\n",
       "      <td>(35.5, 45.5]</td>\n",
       "      <td>8522</td>\n",
       "      <td>1861</td>\n",
       "      <td>6661</td>\n",
       "      <td>28.4067%</td>\n",
       "      <td>28.0440%</td>\n",
       "      <td>28.5097%</td>\n",
       "      <td>21.8376%</td>\n",
       "      <td>-0.0165</td>\n",
       "      <td>0.0001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>AGE</td>\n",
       "      <td>5</td>\n",
       "      <td>(45.5, inf]</td>\n",
       "      <td>4669</td>\n",
       "      <td>1178</td>\n",
       "      <td>3491</td>\n",
       "      <td>15.5633%</td>\n",
       "      <td>17.7517%</td>\n",
       "      <td>14.9418%</td>\n",
       "      <td>25.2302%</td>\n",
       "      <td>0.1723</td>\n",
       "      <td>0.0048</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Total</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>30000</td>\n",
       "      <td>6636</td>\n",
       "      <td>23364</td>\n",
       "      <td>100.0000%</td>\n",
       "      <td>100.0000%</td>\n",
       "      <td>100.0000%</td>\n",
       "      <td>22.1200%</td>\n",
       "      <td>0.1494</td>\n",
       "      <td>0.0217</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Name No.           Bin  #Total  #Bad  #Good     %Total       %Bad  \\\n",
       "0    AGE   1  (-inf, 25.5]    3871  1032   2839   12.9033%   15.5515%   \n",
       "1    AGE   2  (25.5, 28.5]    4142   852   3290   13.8067%   12.8391%   \n",
       "2    AGE   3  (28.5, 35.5]    8796  1713   7083   29.3200%   25.8137%   \n",
       "3    AGE   4  (35.5, 45.5]    8522  1861   6661   28.4067%   28.0440%   \n",
       "4    AGE   5   (45.5, inf]    4669  1178   3491   15.5633%   17.7517%   \n",
       "5  Total                     30000  6636  23364  100.0000%  100.0000%   \n",
       "\n",
       "       %Good  %BadRate      WoE      IV  \n",
       "0   12.1512%  26.6598%   0.2467  0.0084  \n",
       "1   14.0815%  20.5698%  -0.0924  0.0011  \n",
       "2   30.3159%  19.4748%  -0.1608  0.0072  \n",
       "3   28.5097%  21.8376%  -0.0165  0.0001  \n",
       "4   14.9418%  25.2302%   0.1723  0.0048  \n",
       "5  100.0000%  22.1200%   0.1494  0.0217  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "woe_iv(data,col='AGE')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fe003b01",
   "metadata": {},
   "source": [
    "### 等频分箱"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bb9a1c3e",
   "metadata": {},
   "source": [
    "使用westat进行等频分箱时，需要传入目标数据集和需要进行分箱的特征变量名称，另外也需要指定qcut参数，用来设置需要分箱数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "e1183799",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>No.</th>\n",
       "      <th>Bin</th>\n",
       "      <th>#Total</th>\n",
       "      <th>#Bad</th>\n",
       "      <th>#Good</th>\n",
       "      <th>%Total</th>\n",
       "      <th>%Bad</th>\n",
       "      <th>%Good</th>\n",
       "      <th>%BadRate</th>\n",
       "      <th>WoE</th>\n",
       "      <th>IV</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AGE</td>\n",
       "      <td>1</td>\n",
       "      <td>(20.999, 27.0]</td>\n",
       "      <td>6604</td>\n",
       "      <td>1598</td>\n",
       "      <td>5006</td>\n",
       "      <td>22.01%</td>\n",
       "      <td>24.08%</td>\n",
       "      <td>21.43%</td>\n",
       "      <td>24.20%</td>\n",
       "      <td>0.12</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AGE</td>\n",
       "      <td>2</td>\n",
       "      <td>(27.0, 31.0]</td>\n",
       "      <td>5626</td>\n",
       "      <td>1102</td>\n",
       "      <td>4524</td>\n",
       "      <td>18.75%</td>\n",
       "      <td>16.61%</td>\n",
       "      <td>19.36%</td>\n",
       "      <td>19.59%</td>\n",
       "      <td>-0.15</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AGE</td>\n",
       "      <td>3</td>\n",
       "      <td>(31.0, 37.0]</td>\n",
       "      <td>6728</td>\n",
       "      <td>1380</td>\n",
       "      <td>5348</td>\n",
       "      <td>22.43%</td>\n",
       "      <td>20.80%</td>\n",
       "      <td>22.89%</td>\n",
       "      <td>20.51%</td>\n",
       "      <td>-0.10</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>AGE</td>\n",
       "      <td>4</td>\n",
       "      <td>(37.0, 43.0]</td>\n",
       "      <td>5056</td>\n",
       "      <td>1100</td>\n",
       "      <td>3956</td>\n",
       "      <td>16.85%</td>\n",
       "      <td>16.58%</td>\n",
       "      <td>16.93%</td>\n",
       "      <td>21.76%</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>AGE</td>\n",
       "      <td>5</td>\n",
       "      <td>(43.0, 79.0]</td>\n",
       "      <td>5986</td>\n",
       "      <td>1456</td>\n",
       "      <td>4530</td>\n",
       "      <td>19.95%</td>\n",
       "      <td>21.94%</td>\n",
       "      <td>19.39%</td>\n",
       "      <td>24.32%</td>\n",
       "      <td>0.12</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Total</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>30000</td>\n",
       "      <td>6636</td>\n",
       "      <td>23364</td>\n",
       "      <td>100.00%</td>\n",
       "      <td>100.00%</td>\n",
       "      <td>100.00%</td>\n",
       "      <td>22.12%</td>\n",
       "      <td>-0.03</td>\n",
       "      <td>0.01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Name No.             Bin  #Total  #Bad  #Good   %Total     %Bad    %Good  \\\n",
       "0    AGE   1  (20.999, 27.0]    6604  1598   5006   22.01%   24.08%   21.43%   \n",
       "1    AGE   2    (27.0, 31.0]    5626  1102   4524   18.75%   16.61%   19.36%   \n",
       "2    AGE   3    (31.0, 37.0]    6728  1380   5348   22.43%   20.80%   22.89%   \n",
       "3    AGE   4    (37.0, 43.0]    5056  1100   3956   16.85%   16.58%   16.93%   \n",
       "4    AGE   5    (43.0, 79.0]    5986  1456   4530   19.95%   21.94%   19.39%   \n",
       "5  Total                       30000  6636  23364  100.00%  100.00%  100.00%   \n",
       "\n",
       "  %BadRate    WoE    IV  \n",
       "0   24.20%   0.12  0.00  \n",
       "1   19.59%  -0.15  0.00  \n",
       "2   20.51%  -0.10  0.00  \n",
       "3   21.76%  -0.02  0.00  \n",
       "4   24.32%   0.12  0.00  \n",
       "5   22.12%  -0.03  0.01  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 等频分箱，计算WoE和IV，默认结果精度保留4位小数，通过 precision = 2 指定结果保留2位小数\n",
    "woe_iv(data,col='AGE',qcut=5,precision=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "745d55d3",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>No.</th>\n",
       "      <th>Bin</th>\n",
       "      <th>#Total</th>\n",
       "      <th>#Bad</th>\n",
       "      <th>#Good</th>\n",
       "      <th>%Total</th>\n",
       "      <th>%Bad</th>\n",
       "      <th>%Good</th>\n",
       "      <th>%BadRate</th>\n",
       "      <th>WoE</th>\n",
       "      <th>IV</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AGE</td>\n",
       "      <td>1</td>\n",
       "      <td>(-inf, 25.5]</td>\n",
       "      <td>3871</td>\n",
       "      <td>1032</td>\n",
       "      <td>2839</td>\n",
       "      <td>12.9033%</td>\n",
       "      <td>15.5515%</td>\n",
       "      <td>12.1512%</td>\n",
       "      <td>26.6598%</td>\n",
       "      <td>0.2467</td>\n",
       "      <td>0.0084</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AGE</td>\n",
       "      <td>2</td>\n",
       "      <td>(25.5, 28.5]</td>\n",
       "      <td>4142</td>\n",
       "      <td>852</td>\n",
       "      <td>3290</td>\n",
       "      <td>13.8067%</td>\n",
       "      <td>12.8391%</td>\n",
       "      <td>14.0815%</td>\n",
       "      <td>20.5698%</td>\n",
       "      <td>-0.0924</td>\n",
       "      <td>0.0011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AGE</td>\n",
       "      <td>3</td>\n",
       "      <td>(28.5, 35.5]</td>\n",
       "      <td>8796</td>\n",
       "      <td>1713</td>\n",
       "      <td>7083</td>\n",
       "      <td>29.3200%</td>\n",
       "      <td>25.8137%</td>\n",
       "      <td>30.3159%</td>\n",
       "      <td>19.4748%</td>\n",
       "      <td>-0.1608</td>\n",
       "      <td>0.0072</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>AGE</td>\n",
       "      <td>4</td>\n",
       "      <td>(35.5, 45.5]</td>\n",
       "      <td>8522</td>\n",
       "      <td>1861</td>\n",
       "      <td>6661</td>\n",
       "      <td>28.4067%</td>\n",
       "      <td>28.0440%</td>\n",
       "      <td>28.5097%</td>\n",
       "      <td>21.8376%</td>\n",
       "      <td>-0.0165</td>\n",
       "      <td>0.0001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>AGE</td>\n",
       "      <td>5</td>\n",
       "      <td>(45.5, inf]</td>\n",
       "      <td>4669</td>\n",
       "      <td>1178</td>\n",
       "      <td>3491</td>\n",
       "      <td>15.5633%</td>\n",
       "      <td>17.7517%</td>\n",
       "      <td>14.9418%</td>\n",
       "      <td>25.2302%</td>\n",
       "      <td>0.1723</td>\n",
       "      <td>0.0048</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Total</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>30000</td>\n",
       "      <td>6636</td>\n",
       "      <td>23364</td>\n",
       "      <td>100.0000%</td>\n",
       "      <td>100.0000%</td>\n",
       "      <td>100.0000%</td>\n",
       "      <td>22.1200%</td>\n",
       "      <td>0.1494</td>\n",
       "      <td>0.0217</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Name No.           Bin  #Total  #Bad  #Good     %Total       %Bad  \\\n",
       "0    AGE   1  (-inf, 25.5]    3871  1032   2839   12.9033%   15.5515%   \n",
       "1    AGE   2  (25.5, 28.5]    4142   852   3290   13.8067%   12.8391%   \n",
       "2    AGE   3  (28.5, 35.5]    8796  1713   7083   29.3200%   25.8137%   \n",
       "3    AGE   4  (35.5, 45.5]    8522  1861   6661   28.4067%   28.0440%   \n",
       "4    AGE   5   (45.5, inf]    4669  1178   3491   15.5633%   17.7517%   \n",
       "5  Total                     30000  6636  23364  100.0000%  100.0000%   \n",
       "\n",
       "       %Good  %BadRate      WoE      IV  \n",
       "0   12.1512%  26.6598%   0.2467  0.0084  \n",
       "1   14.0815%  20.5698%  -0.0924  0.0011  \n",
       "2   30.3159%  19.4748%  -0.1608  0.0072  \n",
       "3   28.5097%  21.8376%  -0.0165  0.0001  \n",
       "4   14.9418%  25.2302%   0.1723  0.0048  \n",
       "5  100.0000%  22.1200%   0.1494  0.0217  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#使用默认的决策树方法进行分箱\n",
    "woe_iv(data,col='AGE')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "46729710",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "      <th>0</th>\n",
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       "      <td>3871</td>\n",
       "      <td>1032</td>\n",
       "      <td>2839</td>\n",
       "      <td>12.9033%</td>\n",
       "      <td>15.5515%</td>\n",
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       "      <td>3290</td>\n",
       "      <td>13.8067%</td>\n",
       "      <td>12.8391%</td>\n",
       "      <td>14.0815%</td>\n",
       "      <td>20.5698%</td>\n",
       "      <td>-0.0924</td>\n",
       "      <td>0.0011</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
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       "      <td>8796</td>\n",
       "      <td>1713</td>\n",
       "      <td>7083</td>\n",
       "      <td>29.3200%</td>\n",
       "      <td>25.8137%</td>\n",
       "      <td>30.3159%</td>\n",
       "      <td>19.4748%</td>\n",
       "      <td>-0.1608</td>\n",
       "      <td>0.0072</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>AGE</td>\n",
       "      <td>4</td>\n",
       "      <td>(35.5, 45.5]</td>\n",
       "      <td>8522</td>\n",
       "      <td>1861</td>\n",
       "      <td>6661</td>\n",
       "      <td>28.4067%</td>\n",
       "      <td>28.0440%</td>\n",
       "      <td>28.5097%</td>\n",
       "      <td>21.8376%</td>\n",
       "      <td>-0.0165</td>\n",
       "      <td>0.0001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>AGE</td>\n",
       "      <td>5</td>\n",
       "      <td>(45.5, inf]</td>\n",
       "      <td>4669</td>\n",
       "      <td>1178</td>\n",
       "      <td>3491</td>\n",
       "      <td>15.5633%</td>\n",
       "      <td>17.7517%</td>\n",
       "      <td>14.9418%</td>\n",
       "      <td>25.2302%</td>\n",
       "      <td>0.1723</td>\n",
       "      <td>0.0048</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Total</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>30000</td>\n",
       "      <td>6636</td>\n",
       "      <td>23364</td>\n",
       "      <td>100.0000%</td>\n",
       "      <td>100.0000%</td>\n",
       "      <td>100.0000%</td>\n",
       "      <td>22.1200%</td>\n",
       "      <td>0.1494</td>\n",
       "      <td>0.0217</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Name No.           Bin  #Total  #Bad  #Good     %Total       %Bad  \\\n",
       "0    AGE   1  (-inf, 25.5]    3871  1032   2839   12.9033%   15.5515%   \n",
       "1    AGE   2  (25.5, 28.5]    4142   852   3290   13.8067%   12.8391%   \n",
       "2    AGE   3  (28.5, 35.5]    8796  1713   7083   29.3200%   25.8137%   \n",
       "3    AGE   4  (35.5, 45.5]    8522  1861   6661   28.4067%   28.0440%   \n",
       "4    AGE   5   (45.5, inf]    4669  1178   3491   15.5633%   17.7517%   \n",
       "5  Total                     30000  6636  23364  100.0000%  100.0000%   \n",
       "\n",
       "       %Good  %BadRate      WoE      IV  \n",
       "0   12.1512%  26.6598%   0.2467  0.0084  \n",
       "1   14.0815%  20.5698%  -0.0924  0.0011  \n",
       "2   30.3159%  19.4748%  -0.1608  0.0072  \n",
       "3   28.5097%  21.8376%  -0.0165  0.0001  \n",
       "4   14.9418%  25.2302%   0.1723  0.0048  \n",
       "5  100.0000%  22.1200%   0.1494  0.0217  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 设置输出的分箱小数点位数\n",
    "woe_iv(data,'AGE',precision=4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "91291c68",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
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       "      <td>2471</td>\n",
       "      <td>8542</td>\n",
       "      <td>36.7100%</td>\n",
       "      <td>37.2363%</td>\n",
       "      <td>36.5605%</td>\n",
       "      <td>22.4371%</td>\n",
       "      <td>0.0183</td>\n",
       "      <td>0.0001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AGE</td>\n",
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       "      <td>10713</td>\n",
       "      <td>2189</td>\n",
       "      <td>8524</td>\n",
       "      <td>35.7100%</td>\n",
       "      <td>32.9867%</td>\n",
       "      <td>36.4835%</td>\n",
       "      <td>20.4331%</td>\n",
       "      <td>-0.1008</td>\n",
       "      <td>0.0035</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>AGE</td>\n",
       "      <td>4</td>\n",
       "      <td>(40.0, 50.0]</td>\n",
       "      <td>6005</td>\n",
       "      <td>1399</td>\n",
       "      <td>4606</td>\n",
       "      <td>20.0167%</td>\n",
       "      <td>21.0820%</td>\n",
       "      <td>19.7141%</td>\n",
       "      <td>23.2973%</td>\n",
       "      <td>0.0671</td>\n",
       "      <td>0.0009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>AGE</td>\n",
       "      <td>5</td>\n",
       "      <td>(50.0, inf]</td>\n",
       "      <td>2269</td>\n",
       "      <td>577</td>\n",
       "      <td>1692</td>\n",
       "      <td>7.5633%</td>\n",
       "      <td>8.6950%</td>\n",
       "      <td>7.2419%</td>\n",
       "      <td>25.4297%</td>\n",
       "      <td>0.1829</td>\n",
       "      <td>0.0027</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Total</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>30000</td>\n",
       "      <td>6636</td>\n",
       "      <td>23364</td>\n",
       "      <td>100.0000%</td>\n",
       "      <td>100.0000%</td>\n",
       "      <td>100.0000%</td>\n",
       "      <td>22.1200%</td>\n",
       "      <td>0.1675</td>\n",
       "      <td>0.0072</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Name No.           Bin  #Total  #Bad  #Good     %Total       %Bad  \\\n",
       "0    AGE   1  (-inf, 20.0]       0     0      0    0.0000%    0.0000%   \n",
       "1    AGE   2  (20.0, 30.0]   11013  2471   8542   36.7100%   37.2363%   \n",
       "2    AGE   3  (30.0, 40.0]   10713  2189   8524   35.7100%   32.9867%   \n",
       "3    AGE   4  (40.0, 50.0]    6005  1399   4606   20.0167%   21.0820%   \n",
       "4    AGE   5   (50.0, inf]    2269   577   1692    7.5633%    8.6950%   \n",
       "5  Total                     30000  6636  23364  100.0000%  100.0000%   \n",
       "\n",
       "       %Good  %BadRate      WoE      IV  \n",
       "0    0.0000%   0.0000%   0.0000  0.0000  \n",
       "1   36.5605%  22.4371%   0.0183  0.0001  \n",
       "2   36.4835%  20.4331%  -0.1008  0.0035  \n",
       "3   19.7141%  23.2973%   0.0671  0.0009  \n",
       "4    7.2419%  25.4297%   0.1829  0.0027  \n",
       "5  100.0000%  22.1200%   0.1675  0.0072  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 按照指定的切分点 分箱，并计算 WoE 和 IV\n",
    "woe_iv(data,'AGE',bins=[-inf,20,30,40,50,inf])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "3685706f",
   "metadata": {},
   "outputs": [
    {
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       "      <th>#Bad</th>\n",
       "      <th>#Good</th>\n",
       "      <th>%Total</th>\n",
       "      <th>%Bad</th>\n",
       "      <th>%Good</th>\n",
       "      <th>%BadRate</th>\n",
       "      <th>WoE</th>\n",
       "      <th>IV</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>bin_age</td>\n",
       "      <td>1</td>\n",
       "      <td>(-inf, 25.0]</td>\n",
       "      <td>3871</td>\n",
       "      <td>1032</td>\n",
       "      <td>2839</td>\n",
       "      <td>12.9033%</td>\n",
       "      <td>15.5515%</td>\n",
       "      <td>12.1512%</td>\n",
       "      <td>26.6598%</td>\n",
       "      <td>0.2467</td>\n",
       "      <td>0.0084</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>bin_age</td>\n",
       "      <td>2</td>\n",
       "      <td>(25.0, 40.0]</td>\n",
       "      <td>17855</td>\n",
       "      <td>3628</td>\n",
       "      <td>14227</td>\n",
       "      <td>59.5167%</td>\n",
       "      <td>54.6715%</td>\n",
       "      <td>60.8928%</td>\n",
       "      <td>20.3192%</td>\n",
       "      <td>-0.1078</td>\n",
       "      <td>0.0067</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>bin_age</td>\n",
       "      <td>3</td>\n",
       "      <td>(40.0, 50.0]</td>\n",
       "      <td>6005</td>\n",
       "      <td>1399</td>\n",
       "      <td>4606</td>\n",
       "      <td>20.0167%</td>\n",
       "      <td>21.0820%</td>\n",
       "      <td>19.7141%</td>\n",
       "      <td>23.2973%</td>\n",
       "      <td>0.0671</td>\n",
       "      <td>0.0009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>bin_age</td>\n",
       "      <td>4</td>\n",
       "      <td>(50.0, 60.0]</td>\n",
       "      <td>1997</td>\n",
       "      <td>504</td>\n",
       "      <td>1493</td>\n",
       "      <td>6.6567%</td>\n",
       "      <td>7.5949%</td>\n",
       "      <td>6.3902%</td>\n",
       "      <td>25.2379%</td>\n",
       "      <td>0.1727</td>\n",
       "      <td>0.0021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>bin_age</td>\n",
       "      <td>5</td>\n",
       "      <td>(60.0, 70.0]</td>\n",
       "      <td>257</td>\n",
       "      <td>68</td>\n",
       "      <td>189</td>\n",
       "      <td>0.8567%</td>\n",
       "      <td>1.0247%</td>\n",
       "      <td>0.8089%</td>\n",
       "      <td>26.4591%</td>\n",
       "      <td>0.2364</td>\n",
       "      <td>0.0005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>bin_age</td>\n",
       "      <td>6</td>\n",
       "      <td>(70.0, inf]</td>\n",
       "      <td>15</td>\n",
       "      <td>5</td>\n",
       "      <td>10</td>\n",
       "      <td>0.0500%</td>\n",
       "      <td>0.0753%</td>\n",
       "      <td>0.0428%</td>\n",
       "      <td>33.3333%</td>\n",
       "      <td>0.5655</td>\n",
       "      <td>0.0002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Total</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>30000</td>\n",
       "      <td>6636</td>\n",
       "      <td>23364</td>\n",
       "      <td>100.0000%</td>\n",
       "      <td>100.0000%</td>\n",
       "      <td>100.0000%</td>\n",
       "      <td>22.1200%</td>\n",
       "      <td>1.1808</td>\n",
       "      <td>0.0188</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Name No.           Bin  #Total  #Bad  #Good     %Total       %Bad  \\\n",
       "0  bin_age   1  (-inf, 25.0]    3871  1032   2839   12.9033%   15.5515%   \n",
       "1  bin_age   2  (25.0, 40.0]   17855  3628  14227   59.5167%   54.6715%   \n",
       "2  bin_age   3  (40.0, 50.0]    6005  1399   4606   20.0167%   21.0820%   \n",
       "3  bin_age   4  (50.0, 60.0]    1997   504   1493    6.6567%    7.5949%   \n",
       "4  bin_age   5  (60.0, 70.0]     257    68    189    0.8567%    1.0247%   \n",
       "5  bin_age   6   (70.0, inf]      15     5     10    0.0500%    0.0753%   \n",
       "6    Total                     30000  6636  23364  100.0000%  100.0000%   \n",
       "\n",
       "       %Good  %BadRate      WoE      IV  \n",
       "0   12.1512%  26.6598%   0.2467  0.0084  \n",
       "1   60.8928%  20.3192%  -0.1078  0.0067  \n",
       "2   19.7141%  23.2973%   0.0671  0.0009  \n",
       "3    6.3902%  25.2379%   0.1727  0.0021  \n",
       "4    0.8089%  26.4591%   0.2364  0.0005  \n",
       "5    0.0428%  33.3333%   0.5655  0.0002  \n",
       "6  100.0000%  22.1200%   1.1808  0.0188  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 使用已经分箱后的数据计算woe和iv\n",
    "age_bins = [-inf, 25, 40, 50, 60, 70, inf]\n",
    "data['bin_age'] = pd.cut(data['AGE'],bins=age_bins).astype(str)\n",
    "\n",
    "woe_iv(data,'bin_age',method='discrete')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "1505aa81",
   "metadata": {},
   "outputs": [],
   "source": [
    "data.drop(columns=['bin_age'],inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "55f72fb9",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>PAY_0</td>\n",
       "      <td>1</td>\n",
       "      <td>(-inf, -2.0]</td>\n",
       "      <td>2759</td>\n",
       "      <td>365</td>\n",
       "      <td>2394</td>\n",
       "      <td>9.1967%</td>\n",
       "      <td>5.5003%</td>\n",
       "      <td>10.2465%</td>\n",
       "      <td>13.2294%</td>\n",
       "      <td>-0.6221</td>\n",
       "      <td>0.0295</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>PAY_0</td>\n",
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       "      <td>5686</td>\n",
       "      <td>954</td>\n",
       "      <td>4732</td>\n",
       "      <td>18.9533%</td>\n",
       "      <td>14.3761%</td>\n",
       "      <td>20.2534%</td>\n",
       "      <td>16.7781%</td>\n",
       "      <td>-0.3428</td>\n",
       "      <td>0.0201</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>PAY_0</td>\n",
       "      <td>3</td>\n",
       "      <td>(-1.0, 0.0]</td>\n",
       "      <td>14737</td>\n",
       "      <td>1888</td>\n",
       "      <td>12849</td>\n",
       "      <td>49.1233%</td>\n",
       "      <td>28.4509%</td>\n",
       "      <td>54.9949%</td>\n",
       "      <td>12.8113%</td>\n",
       "      <td>-0.6591</td>\n",
       "      <td>0.1749</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>PAY_0</td>\n",
       "      <td>4</td>\n",
       "      <td>(0.0, 1.0]</td>\n",
       "      <td>3688</td>\n",
       "      <td>1252</td>\n",
       "      <td>2436</td>\n",
       "      <td>12.2933%</td>\n",
       "      <td>18.8668%</td>\n",
       "      <td>10.4263%</td>\n",
       "      <td>33.9479%</td>\n",
       "      <td>0.5931</td>\n",
       "      <td>0.0501</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>PAY_0</td>\n",
       "      <td>5</td>\n",
       "      <td>(1.0, inf]</td>\n",
       "      <td>3130</td>\n",
       "      <td>2177</td>\n",
       "      <td>953</td>\n",
       "      <td>10.4333%</td>\n",
       "      <td>32.8059%</td>\n",
       "      <td>4.0789%</td>\n",
       "      <td>69.5527%</td>\n",
       "      <td>2.0848</td>\n",
       "      <td>0.5989</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Total</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>30000</td>\n",
       "      <td>6636</td>\n",
       "      <td>23364</td>\n",
       "      <td>100.0000%</td>\n",
       "      <td>100.0000%</td>\n",
       "      <td>100.0000%</td>\n",
       "      <td>22.1200%</td>\n",
       "      <td>1.0539</td>\n",
       "      <td>0.8736</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Name No.           Bin  #Total  #Bad  #Good     %Total       %Bad  \\\n",
       "0  PAY_0   1  (-inf, -2.0]    2759   365   2394    9.1967%    5.5003%   \n",
       "1  PAY_0   2  (-2.0, -1.0]    5686   954   4732   18.9533%   14.3761%   \n",
       "2  PAY_0   3   (-1.0, 0.0]   14737  1888  12849   49.1233%   28.4509%   \n",
       "3  PAY_0   4    (0.0, 1.0]    3688  1252   2436   12.2933%   18.8668%   \n",
       "4  PAY_0   5    (1.0, inf]    3130  2177    953   10.4333%   32.8059%   \n",
       "5  Total                     30000  6636  23364  100.0000%  100.0000%   \n",
       "\n",
       "       %Good  %BadRate      WoE      IV  \n",
       "0   10.2465%  13.2294%  -0.6221  0.0295  \n",
       "1   20.2534%  16.7781%  -0.3428  0.0201  \n",
       "2   54.9949%  12.8113%  -0.6591  0.1749  \n",
       "3   10.4263%  33.9479%   0.5931  0.0501  \n",
       "4    4.0789%  69.5527%   2.0848  0.5989  \n",
       "5  100.0000%  22.1200%   1.0539  0.8736  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 使用决策树方法计算计算 WoE 和 IV\n",
    "woe_iv(data,'PAY_0',method='tree')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "9a43f50f",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\about\\AppData\\Local\\Temp\\ipykernel_16688\\1355770748.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  data['PAY_0'][0] = np.nan\n"
     ]
    },
    {
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       "      <th>Name</th>\n",
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       "      <th>#Bad</th>\n",
       "      <th>#Good</th>\n",
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       "      <th>%Bad</th>\n",
       "      <th>%Good</th>\n",
       "      <th>%BadRate</th>\n",
       "      <th>WoE</th>\n",
       "      <th>IV</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>PAY_0</td>\n",
       "      <td>1</td>\n",
       "      <td>missing</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0067%</td>\n",
       "      <td>0.0301%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>100.0000%</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>PAY_0</td>\n",
       "      <td>2</td>\n",
       "      <td>(-inf, -0.5]</td>\n",
       "      <td>8444</td>\n",
       "      <td>1318</td>\n",
       "      <td>7126</td>\n",
       "      <td>28.1467%</td>\n",
       "      <td>19.8614%</td>\n",
       "      <td>30.4999%</td>\n",
       "      <td>15.6087%</td>\n",
       "      <td>-0.4289</td>\n",
       "      <td>0.0456</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>PAY_0</td>\n",
       "      <td>3</td>\n",
       "      <td>(-0.5, 0.5]</td>\n",
       "      <td>14737</td>\n",
       "      <td>1888</td>\n",
       "      <td>12849</td>\n",
       "      <td>49.1233%</td>\n",
       "      <td>28.4509%</td>\n",
       "      <td>54.9949%</td>\n",
       "      <td>12.8113%</td>\n",
       "      <td>-0.6591</td>\n",
       "      <td>0.1749</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>PAY_0</td>\n",
       "      <td>4</td>\n",
       "      <td>(0.5, 1.5]</td>\n",
       "      <td>3688</td>\n",
       "      <td>1252</td>\n",
       "      <td>2436</td>\n",
       "      <td>12.2933%</td>\n",
       "      <td>18.8668%</td>\n",
       "      <td>10.4263%</td>\n",
       "      <td>33.9479%</td>\n",
       "      <td>0.5931</td>\n",
       "      <td>0.0501</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>PAY_0</td>\n",
       "      <td>5</td>\n",
       "      <td>(1.5, inf]</td>\n",
       "      <td>3129</td>\n",
       "      <td>2176</td>\n",
       "      <td>953</td>\n",
       "      <td>10.4300%</td>\n",
       "      <td>32.7908%</td>\n",
       "      <td>4.0789%</td>\n",
       "      <td>69.5430%</td>\n",
       "      <td>2.0843</td>\n",
       "      <td>0.5984</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Total</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>30000</td>\n",
       "      <td>6636</td>\n",
       "      <td>23364</td>\n",
       "      <td>100.0000%</td>\n",
       "      <td>100.0000%</td>\n",
       "      <td>100.0000%</td>\n",
       "      <td>22.1200%</td>\n",
       "      <td>1.5894</td>\n",
       "      <td>0.8691</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Name No.           Bin  #Total  #Bad  #Good     %Total       %Bad  \\\n",
       "0  PAY_0   1       missing       2     2      0    0.0067%    0.0301%   \n",
       "1  PAY_0   2  (-inf, -0.5]    8444  1318   7126   28.1467%   19.8614%   \n",
       "2  PAY_0   3   (-0.5, 0.5]   14737  1888  12849   49.1233%   28.4509%   \n",
       "3  PAY_0   4    (0.5, 1.5]    3688  1252   2436   12.2933%   18.8668%   \n",
       "4  PAY_0   5    (1.5, inf]    3129  2176    953   10.4300%   32.7908%   \n",
       "5  Total                     30000  6636  23364  100.0000%  100.0000%   \n",
       "\n",
       "       %Good   %BadRate      WoE      IV  \n",
       "0    0.0000%  100.0000%   0.0000  0.0000  \n",
       "1   30.4999%   15.6087%  -0.4289  0.0456  \n",
       "2   54.9949%   12.8113%  -0.6591  0.1749  \n",
       "3   10.4263%   33.9479%   0.5931  0.0501  \n",
       "4    4.0789%   69.5430%   2.0843  0.5984  \n",
       "5  100.0000%   22.1200%   1.5894  0.8691  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 指定缺失值类型，并使用默认的决策树分箱方法进行分箱，计算WoE和IV, \n",
    "data['PAY_0'][0] = np.nan\n",
    "data['PAY_0'][1] = -99999\n",
    "\n",
    "woe_iv(data,col='PAY_0',missing = [-99999])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "44a3316c",
   "metadata": {},
   "source": [
    "## 查看WoE 和 IV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "1446c0eb",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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       "#T_8cbae_row0_col12 {\n",
       "  width: 10em;\n",
       "  background: linear-gradient(90deg, transparent 39.5%, #007bff 39.5%, #007bff 100.0%, transparent 100.0%);\n",
       "}\n",
       "#T_8cbae_row1_col12 {\n",
       "  width: 10em;\n",
       "  background: linear-gradient(90deg, transparent 16.8%, #007bff 16.8%, #007bff 39.5%, transparent 39.5%);\n",
       "}\n",
       "#T_8cbae_row2_col12 {\n",
       "  width: 10em;\n",
       "  background: linear-gradient(90deg, #007bff 39.5%, transparent 39.5%);\n",
       "}\n",
       "#T_8cbae_row3_col12 {\n",
       "  width: 10em;\n",
       "  background: linear-gradient(90deg, transparent 35.4%, #007bff 35.4%, #007bff 39.5%, transparent 39.5%);\n",
       "}\n",
       "#T_8cbae_row4_col12 {\n",
       "  width: 10em;\n",
       "  background: linear-gradient(90deg, transparent 39.5%, #007bff 39.5%, #007bff 81.7%, transparent 81.7%);\n",
       "}\n",
       "#T_8cbae_row5_col12 {\n",
       "  width: 10em;\n",
       "  background: linear-gradient(90deg, transparent 39.5%, #007bff 39.5%, #007bff 76.1%, transparent 76.1%);\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_8cbae\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_8cbae_level0_col0\" class=\"col_heading level0 col0\" >Name</th>\n",
       "      <th id=\"T_8cbae_level0_col1\" class=\"col_heading level0 col1\" >No.</th>\n",
       "      <th id=\"T_8cbae_level0_col2\" class=\"col_heading level0 col2\" >Bin</th>\n",
       "      <th id=\"T_8cbae_level0_col3\" class=\"col_heading level0 col3\" >#Total</th>\n",
       "      <th id=\"T_8cbae_level0_col4\" class=\"col_heading level0 col4\" >#Bad</th>\n",
       "      <th id=\"T_8cbae_level0_col5\" class=\"col_heading level0 col5\" >#Good</th>\n",
       "      <th id=\"T_8cbae_level0_col6\" class=\"col_heading level0 col6\" >%Total</th>\n",
       "      <th id=\"T_8cbae_level0_col7\" class=\"col_heading level0 col7\" >%Bad</th>\n",
       "      <th id=\"T_8cbae_level0_col8\" class=\"col_heading level0 col8\" >%Good</th>\n",
       "      <th id=\"T_8cbae_level0_col9\" class=\"col_heading level0 col9\" >%BadRate</th>\n",
       "      <th id=\"T_8cbae_level0_col10\" class=\"col_heading level0 col10\" >WoE</th>\n",
       "      <th id=\"T_8cbae_level0_col11\" class=\"col_heading level0 col11\" >IV</th>\n",
       "      <th id=\"T_8cbae_level0_col12\" class=\"col_heading level0 col12\" >WoE.</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_8cbae_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "      <td id=\"T_8cbae_row0_col0\" class=\"data row0 col0\" >AGE</td>\n",
       "      <td id=\"T_8cbae_row0_col1\" class=\"data row0 col1\" >1</td>\n",
       "      <td id=\"T_8cbae_row0_col2\" class=\"data row0 col2\" >(-inf, 25.5]</td>\n",
       "      <td id=\"T_8cbae_row0_col3\" class=\"data row0 col3\" >3871</td>\n",
       "      <td id=\"T_8cbae_row0_col4\" class=\"data row0 col4\" >1032</td>\n",
       "      <td id=\"T_8cbae_row0_col5\" class=\"data row0 col5\" >2839</td>\n",
       "      <td id=\"T_8cbae_row0_col6\" class=\"data row0 col6\" >12.9033%</td>\n",
       "      <td id=\"T_8cbae_row0_col7\" class=\"data row0 col7\" >15.5515%</td>\n",
       "      <td id=\"T_8cbae_row0_col8\" class=\"data row0 col8\" >12.1512%</td>\n",
       "      <td id=\"T_8cbae_row0_col9\" class=\"data row0 col9\" >26.6598%</td>\n",
       "      <td id=\"T_8cbae_row0_col10\" class=\"data row0 col10\" >0.2467</td>\n",
       "      <td id=\"T_8cbae_row0_col11\" class=\"data row0 col11\" >0.0084</td>\n",
       "      <td id=\"T_8cbae_row0_col12\" class=\"data row0 col12\" >0.246700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_8cbae_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
       "      <td id=\"T_8cbae_row1_col0\" class=\"data row1 col0\" >AGE</td>\n",
       "      <td id=\"T_8cbae_row1_col1\" class=\"data row1 col1\" >2</td>\n",
       "      <td id=\"T_8cbae_row1_col2\" class=\"data row1 col2\" >(25.5, 28.5]</td>\n",
       "      <td id=\"T_8cbae_row1_col3\" class=\"data row1 col3\" >4142</td>\n",
       "      <td id=\"T_8cbae_row1_col4\" class=\"data row1 col4\" >852</td>\n",
       "      <td id=\"T_8cbae_row1_col5\" class=\"data row1 col5\" >3290</td>\n",
       "      <td id=\"T_8cbae_row1_col6\" class=\"data row1 col6\" >13.8067%</td>\n",
       "      <td id=\"T_8cbae_row1_col7\" class=\"data row1 col7\" >12.8391%</td>\n",
       "      <td id=\"T_8cbae_row1_col8\" class=\"data row1 col8\" >14.0815%</td>\n",
       "      <td id=\"T_8cbae_row1_col9\" class=\"data row1 col9\" >20.5698%</td>\n",
       "      <td id=\"T_8cbae_row1_col10\" class=\"data row1 col10\" >-0.0924</td>\n",
       "      <td id=\"T_8cbae_row1_col11\" class=\"data row1 col11\" >0.0011</td>\n",
       "      <td id=\"T_8cbae_row1_col12\" class=\"data row1 col12\" >-0.092400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_8cbae_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
       "      <td id=\"T_8cbae_row2_col0\" class=\"data row2 col0\" >AGE</td>\n",
       "      <td id=\"T_8cbae_row2_col1\" class=\"data row2 col1\" >3</td>\n",
       "      <td id=\"T_8cbae_row2_col2\" class=\"data row2 col2\" >(28.5, 35.5]</td>\n",
       "      <td id=\"T_8cbae_row2_col3\" class=\"data row2 col3\" >8796</td>\n",
       "      <td id=\"T_8cbae_row2_col4\" class=\"data row2 col4\" >1713</td>\n",
       "      <td id=\"T_8cbae_row2_col5\" class=\"data row2 col5\" >7083</td>\n",
       "      <td id=\"T_8cbae_row2_col6\" class=\"data row2 col6\" >29.3200%</td>\n",
       "      <td id=\"T_8cbae_row2_col7\" class=\"data row2 col7\" >25.8137%</td>\n",
       "      <td id=\"T_8cbae_row2_col8\" class=\"data row2 col8\" >30.3159%</td>\n",
       "      <td id=\"T_8cbae_row2_col9\" class=\"data row2 col9\" >19.4748%</td>\n",
       "      <td id=\"T_8cbae_row2_col10\" class=\"data row2 col10\" >-0.1608</td>\n",
       "      <td id=\"T_8cbae_row2_col11\" class=\"data row2 col11\" >0.0072</td>\n",
       "      <td id=\"T_8cbae_row2_col12\" class=\"data row2 col12\" >-0.160800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_8cbae_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
       "      <td id=\"T_8cbae_row3_col0\" class=\"data row3 col0\" >AGE</td>\n",
       "      <td id=\"T_8cbae_row3_col1\" class=\"data row3 col1\" >4</td>\n",
       "      <td id=\"T_8cbae_row3_col2\" class=\"data row3 col2\" >(35.5, 45.5]</td>\n",
       "      <td id=\"T_8cbae_row3_col3\" class=\"data row3 col3\" >8522</td>\n",
       "      <td id=\"T_8cbae_row3_col4\" class=\"data row3 col4\" >1861</td>\n",
       "      <td id=\"T_8cbae_row3_col5\" class=\"data row3 col5\" >6661</td>\n",
       "      <td id=\"T_8cbae_row3_col6\" class=\"data row3 col6\" >28.4067%</td>\n",
       "      <td id=\"T_8cbae_row3_col7\" class=\"data row3 col7\" >28.0440%</td>\n",
       "      <td id=\"T_8cbae_row3_col8\" class=\"data row3 col8\" >28.5097%</td>\n",
       "      <td id=\"T_8cbae_row3_col9\" class=\"data row3 col9\" >21.8376%</td>\n",
       "      <td id=\"T_8cbae_row3_col10\" class=\"data row3 col10\" >-0.0165</td>\n",
       "      <td id=\"T_8cbae_row3_col11\" class=\"data row3 col11\" >0.0001</td>\n",
       "      <td id=\"T_8cbae_row3_col12\" class=\"data row3 col12\" >-0.016500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_8cbae_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
       "      <td id=\"T_8cbae_row4_col0\" class=\"data row4 col0\" >AGE</td>\n",
       "      <td id=\"T_8cbae_row4_col1\" class=\"data row4 col1\" >5</td>\n",
       "      <td id=\"T_8cbae_row4_col2\" class=\"data row4 col2\" >(45.5, inf]</td>\n",
       "      <td id=\"T_8cbae_row4_col3\" class=\"data row4 col3\" >4669</td>\n",
       "      <td id=\"T_8cbae_row4_col4\" class=\"data row4 col4\" >1178</td>\n",
       "      <td id=\"T_8cbae_row4_col5\" class=\"data row4 col5\" >3491</td>\n",
       "      <td id=\"T_8cbae_row4_col6\" class=\"data row4 col6\" >15.5633%</td>\n",
       "      <td id=\"T_8cbae_row4_col7\" class=\"data row4 col7\" >17.7517%</td>\n",
       "      <td id=\"T_8cbae_row4_col8\" class=\"data row4 col8\" >14.9418%</td>\n",
       "      <td id=\"T_8cbae_row4_col9\" class=\"data row4 col9\" >25.2302%</td>\n",
       "      <td id=\"T_8cbae_row4_col10\" class=\"data row4 col10\" >0.1723</td>\n",
       "      <td id=\"T_8cbae_row4_col11\" class=\"data row4 col11\" >0.0048</td>\n",
       "      <td id=\"T_8cbae_row4_col12\" class=\"data row4 col12\" >0.172300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_8cbae_level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
       "      <td id=\"T_8cbae_row5_col0\" class=\"data row5 col0\" >Total</td>\n",
       "      <td id=\"T_8cbae_row5_col1\" class=\"data row5 col1\" ></td>\n",
       "      <td id=\"T_8cbae_row5_col2\" class=\"data row5 col2\" ></td>\n",
       "      <td id=\"T_8cbae_row5_col3\" class=\"data row5 col3\" >30000</td>\n",
       "      <td id=\"T_8cbae_row5_col4\" class=\"data row5 col4\" >6636</td>\n",
       "      <td id=\"T_8cbae_row5_col5\" class=\"data row5 col5\" >23364</td>\n",
       "      <td id=\"T_8cbae_row5_col6\" class=\"data row5 col6\" >100.0000%</td>\n",
       "      <td id=\"T_8cbae_row5_col7\" class=\"data row5 col7\" >100.0000%</td>\n",
       "      <td id=\"T_8cbae_row5_col8\" class=\"data row5 col8\" >100.0000%</td>\n",
       "      <td id=\"T_8cbae_row5_col9\" class=\"data row5 col9\" >22.1200%</td>\n",
       "      <td id=\"T_8cbae_row5_col10\" class=\"data row5 col10\" >0.1494</td>\n",
       "      <td id=\"T_8cbae_row5_col11\" class=\"data row5 col11\" >0.0217</td>\n",
       "      <td id=\"T_8cbae_row5_col12\" class=\"data row5 col12\" >0.149400</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x290587eb520>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 按照默认的决策树方法，计算WoE 和 IV，并查看WoE的分布\n",
    "view_woe_iv(data,'AGE')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "a8dd5089",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "#T_7b3bf_row0_col12 {\n",
       "  width: 10em;\n",
       "  background: linear-gradient(90deg, transparent 39.5%, green 39.5%, green 100.0%, transparent 100.0%);\n",
       "}\n",
       "#T_7b3bf_row1_col12 {\n",
       "  width: 10em;\n",
       "  background: linear-gradient(90deg, transparent 16.8%, green 16.8%, green 39.5%, transparent 39.5%);\n",
       "}\n",
       "#T_7b3bf_row2_col12 {\n",
       "  width: 10em;\n",
       "  background: linear-gradient(90deg, green 39.5%, transparent 39.5%);\n",
       "}\n",
       "#T_7b3bf_row3_col12 {\n",
       "  width: 10em;\n",
       "  background: linear-gradient(90deg, transparent 35.4%, green 35.4%, green 39.5%, transparent 39.5%);\n",
       "}\n",
       "#T_7b3bf_row4_col12 {\n",
       "  width: 10em;\n",
       "  background: linear-gradient(90deg, transparent 39.5%, green 39.5%, green 81.7%, transparent 81.7%);\n",
       "}\n",
       "#T_7b3bf_row5_col12 {\n",
       "  width: 10em;\n",
       "  background: linear-gradient(90deg, transparent 39.5%, green 39.5%, green 76.1%, transparent 76.1%);\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_7b3bf\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_7b3bf_level0_col0\" class=\"col_heading level0 col0\" >Name</th>\n",
       "      <th id=\"T_7b3bf_level0_col1\" class=\"col_heading level0 col1\" >No.</th>\n",
       "      <th id=\"T_7b3bf_level0_col2\" class=\"col_heading level0 col2\" >Bin</th>\n",
       "      <th id=\"T_7b3bf_level0_col3\" class=\"col_heading level0 col3\" >#Total</th>\n",
       "      <th id=\"T_7b3bf_level0_col4\" class=\"col_heading level0 col4\" >#Bad</th>\n",
       "      <th id=\"T_7b3bf_level0_col5\" class=\"col_heading level0 col5\" >#Good</th>\n",
       "      <th id=\"T_7b3bf_level0_col6\" class=\"col_heading level0 col6\" >%Total</th>\n",
       "      <th id=\"T_7b3bf_level0_col7\" class=\"col_heading level0 col7\" >%Bad</th>\n",
       "      <th id=\"T_7b3bf_level0_col8\" class=\"col_heading level0 col8\" >%Good</th>\n",
       "      <th id=\"T_7b3bf_level0_col9\" class=\"col_heading level0 col9\" >%BadRate</th>\n",
       "      <th id=\"T_7b3bf_level0_col10\" class=\"col_heading level0 col10\" >WoE</th>\n",
       "      <th id=\"T_7b3bf_level0_col11\" class=\"col_heading level0 col11\" >IV</th>\n",
       "      <th id=\"T_7b3bf_level0_col12\" class=\"col_heading level0 col12\" >WoE.</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_7b3bf_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "      <td id=\"T_7b3bf_row0_col0\" class=\"data row0 col0\" >AGE</td>\n",
       "      <td id=\"T_7b3bf_row0_col1\" class=\"data row0 col1\" >1</td>\n",
       "      <td id=\"T_7b3bf_row0_col2\" class=\"data row0 col2\" >(-inf, 25.5]</td>\n",
       "      <td id=\"T_7b3bf_row0_col3\" class=\"data row0 col3\" >3871</td>\n",
       "      <td id=\"T_7b3bf_row0_col4\" class=\"data row0 col4\" >1032</td>\n",
       "      <td id=\"T_7b3bf_row0_col5\" class=\"data row0 col5\" >2839</td>\n",
       "      <td id=\"T_7b3bf_row0_col6\" class=\"data row0 col6\" >12.9033%</td>\n",
       "      <td id=\"T_7b3bf_row0_col7\" class=\"data row0 col7\" >15.5515%</td>\n",
       "      <td id=\"T_7b3bf_row0_col8\" class=\"data row0 col8\" >12.1512%</td>\n",
       "      <td id=\"T_7b3bf_row0_col9\" class=\"data row0 col9\" >26.6598%</td>\n",
       "      <td id=\"T_7b3bf_row0_col10\" class=\"data row0 col10\" >0.2467</td>\n",
       "      <td id=\"T_7b3bf_row0_col11\" class=\"data row0 col11\" >0.0084</td>\n",
       "      <td id=\"T_7b3bf_row0_col12\" class=\"data row0 col12\" >0.246700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7b3bf_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
       "      <td id=\"T_7b3bf_row1_col0\" class=\"data row1 col0\" >AGE</td>\n",
       "      <td id=\"T_7b3bf_row1_col1\" class=\"data row1 col1\" >2</td>\n",
       "      <td id=\"T_7b3bf_row1_col2\" class=\"data row1 col2\" >(25.5, 28.5]</td>\n",
       "      <td id=\"T_7b3bf_row1_col3\" class=\"data row1 col3\" >4142</td>\n",
       "      <td id=\"T_7b3bf_row1_col4\" class=\"data row1 col4\" >852</td>\n",
       "      <td id=\"T_7b3bf_row1_col5\" class=\"data row1 col5\" >3290</td>\n",
       "      <td id=\"T_7b3bf_row1_col6\" class=\"data row1 col6\" >13.8067%</td>\n",
       "      <td id=\"T_7b3bf_row1_col7\" class=\"data row1 col7\" >12.8391%</td>\n",
       "      <td id=\"T_7b3bf_row1_col8\" class=\"data row1 col8\" >14.0815%</td>\n",
       "      <td id=\"T_7b3bf_row1_col9\" class=\"data row1 col9\" >20.5698%</td>\n",
       "      <td id=\"T_7b3bf_row1_col10\" class=\"data row1 col10\" >-0.0924</td>\n",
       "      <td id=\"T_7b3bf_row1_col11\" class=\"data row1 col11\" >0.0011</td>\n",
       "      <td id=\"T_7b3bf_row1_col12\" class=\"data row1 col12\" >-0.092400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7b3bf_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
       "      <td id=\"T_7b3bf_row2_col0\" class=\"data row2 col0\" >AGE</td>\n",
       "      <td id=\"T_7b3bf_row2_col1\" class=\"data row2 col1\" >3</td>\n",
       "      <td id=\"T_7b3bf_row2_col2\" class=\"data row2 col2\" >(28.5, 35.5]</td>\n",
       "      <td id=\"T_7b3bf_row2_col3\" class=\"data row2 col3\" >8796</td>\n",
       "      <td id=\"T_7b3bf_row2_col4\" class=\"data row2 col4\" >1713</td>\n",
       "      <td id=\"T_7b3bf_row2_col5\" class=\"data row2 col5\" >7083</td>\n",
       "      <td id=\"T_7b3bf_row2_col6\" class=\"data row2 col6\" >29.3200%</td>\n",
       "      <td id=\"T_7b3bf_row2_col7\" class=\"data row2 col7\" >25.8137%</td>\n",
       "      <td id=\"T_7b3bf_row2_col8\" class=\"data row2 col8\" >30.3159%</td>\n",
       "      <td id=\"T_7b3bf_row2_col9\" class=\"data row2 col9\" >19.4748%</td>\n",
       "      <td id=\"T_7b3bf_row2_col10\" class=\"data row2 col10\" >-0.1608</td>\n",
       "      <td id=\"T_7b3bf_row2_col11\" class=\"data row2 col11\" >0.0072</td>\n",
       "      <td id=\"T_7b3bf_row2_col12\" class=\"data row2 col12\" >-0.160800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7b3bf_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
       "      <td id=\"T_7b3bf_row3_col0\" class=\"data row3 col0\" >AGE</td>\n",
       "      <td id=\"T_7b3bf_row3_col1\" class=\"data row3 col1\" >4</td>\n",
       "      <td id=\"T_7b3bf_row3_col2\" class=\"data row3 col2\" >(35.5, 45.5]</td>\n",
       "      <td id=\"T_7b3bf_row3_col3\" class=\"data row3 col3\" >8522</td>\n",
       "      <td id=\"T_7b3bf_row3_col4\" class=\"data row3 col4\" >1861</td>\n",
       "      <td id=\"T_7b3bf_row3_col5\" class=\"data row3 col5\" >6661</td>\n",
       "      <td id=\"T_7b3bf_row3_col6\" class=\"data row3 col6\" >28.4067%</td>\n",
       "      <td id=\"T_7b3bf_row3_col7\" class=\"data row3 col7\" >28.0440%</td>\n",
       "      <td id=\"T_7b3bf_row3_col8\" class=\"data row3 col8\" >28.5097%</td>\n",
       "      <td id=\"T_7b3bf_row3_col9\" class=\"data row3 col9\" >21.8376%</td>\n",
       "      <td id=\"T_7b3bf_row3_col10\" class=\"data row3 col10\" >-0.0165</td>\n",
       "      <td id=\"T_7b3bf_row3_col11\" class=\"data row3 col11\" >0.0001</td>\n",
       "      <td id=\"T_7b3bf_row3_col12\" class=\"data row3 col12\" >-0.016500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7b3bf_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
       "      <td id=\"T_7b3bf_row4_col0\" class=\"data row4 col0\" >AGE</td>\n",
       "      <td id=\"T_7b3bf_row4_col1\" class=\"data row4 col1\" >5</td>\n",
       "      <td id=\"T_7b3bf_row4_col2\" class=\"data row4 col2\" >(45.5, inf]</td>\n",
       "      <td id=\"T_7b3bf_row4_col3\" class=\"data row4 col3\" >4669</td>\n",
       "      <td id=\"T_7b3bf_row4_col4\" class=\"data row4 col4\" >1178</td>\n",
       "      <td id=\"T_7b3bf_row4_col5\" class=\"data row4 col5\" >3491</td>\n",
       "      <td id=\"T_7b3bf_row4_col6\" class=\"data row4 col6\" >15.5633%</td>\n",
       "      <td id=\"T_7b3bf_row4_col7\" class=\"data row4 col7\" >17.7517%</td>\n",
       "      <td id=\"T_7b3bf_row4_col8\" class=\"data row4 col8\" >14.9418%</td>\n",
       "      <td id=\"T_7b3bf_row4_col9\" class=\"data row4 col9\" >25.2302%</td>\n",
       "      <td id=\"T_7b3bf_row4_col10\" class=\"data row4 col10\" >0.1723</td>\n",
       "      <td id=\"T_7b3bf_row4_col11\" class=\"data row4 col11\" >0.0048</td>\n",
       "      <td id=\"T_7b3bf_row4_col12\" class=\"data row4 col12\" >0.172300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7b3bf_level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
       "      <td id=\"T_7b3bf_row5_col0\" class=\"data row5 col0\" >Total</td>\n",
       "      <td id=\"T_7b3bf_row5_col1\" class=\"data row5 col1\" ></td>\n",
       "      <td id=\"T_7b3bf_row5_col2\" class=\"data row5 col2\" ></td>\n",
       "      <td id=\"T_7b3bf_row5_col3\" class=\"data row5 col3\" >30000</td>\n",
       "      <td id=\"T_7b3bf_row5_col4\" class=\"data row5 col4\" >6636</td>\n",
       "      <td id=\"T_7b3bf_row5_col5\" class=\"data row5 col5\" >23364</td>\n",
       "      <td id=\"T_7b3bf_row5_col6\" class=\"data row5 col6\" >100.0000%</td>\n",
       "      <td id=\"T_7b3bf_row5_col7\" class=\"data row5 col7\" >100.0000%</td>\n",
       "      <td id=\"T_7b3bf_row5_col8\" class=\"data row5 col8\" >100.0000%</td>\n",
       "      <td id=\"T_7b3bf_row5_col9\" class=\"data row5 col9\" >22.1200%</td>\n",
       "      <td id=\"T_7b3bf_row5_col10\" class=\"data row5 col10\" >0.1494</td>\n",
       "      <td id=\"T_7b3bf_row5_col11\" class=\"data row5 col11\" >0.0217</td>\n",
       "      <td id=\"T_7b3bf_row5_col12\" class=\"data row5 col12\" >0.149400</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x290587eb610>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 按照默认的决策树方法，计算WoE 和 IV，并查看WoE的分布 , 设置显示的颜色为绿色\n",
    "view_woe_iv(data,'AGE',color='green')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "f2bfd530",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "#T_0f3f2_row0_col12 {\n",
       "  width: 10em;\n",
       "  background: linear-gradient(90deg, transparent 39.5%, #02B057 39.5%, #02B057 100.0%, transparent 100.0%);\n",
       "}\n",
       "#T_0f3f2_row1_col12 {\n",
       "  width: 10em;\n",
       "  background: linear-gradient(90deg, transparent 16.8%, #02B057 16.8%, #02B057 39.5%, transparent 39.5%);\n",
       "}\n",
       "#T_0f3f2_row2_col12 {\n",
       "  width: 10em;\n",
       "  background: linear-gradient(90deg, #02B057 39.5%, transparent 39.5%);\n",
       "}\n",
       "#T_0f3f2_row3_col12 {\n",
       "  width: 10em;\n",
       "  background: linear-gradient(90deg, transparent 35.4%, #02B057 35.4%, #02B057 39.5%, transparent 39.5%);\n",
       "}\n",
       "#T_0f3f2_row4_col12 {\n",
       "  width: 10em;\n",
       "  background: linear-gradient(90deg, transparent 39.5%, #02B057 39.5%, #02B057 81.7%, transparent 81.7%);\n",
       "}\n",
       "#T_0f3f2_row5_col12 {\n",
       "  width: 10em;\n",
       "  background: linear-gradient(90deg, transparent 39.5%, #02B057 39.5%, #02B057 76.1%, transparent 76.1%);\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_0f3f2\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_0f3f2_level0_col0\" class=\"col_heading level0 col0\" >Name</th>\n",
       "      <th id=\"T_0f3f2_level0_col1\" class=\"col_heading level0 col1\" >No.</th>\n",
       "      <th id=\"T_0f3f2_level0_col2\" class=\"col_heading level0 col2\" >Bin</th>\n",
       "      <th id=\"T_0f3f2_level0_col3\" class=\"col_heading level0 col3\" >#Total</th>\n",
       "      <th id=\"T_0f3f2_level0_col4\" class=\"col_heading level0 col4\" >#Bad</th>\n",
       "      <th id=\"T_0f3f2_level0_col5\" class=\"col_heading level0 col5\" >#Good</th>\n",
       "      <th id=\"T_0f3f2_level0_col6\" class=\"col_heading level0 col6\" >%Total</th>\n",
       "      <th id=\"T_0f3f2_level0_col7\" class=\"col_heading level0 col7\" >%Bad</th>\n",
       "      <th id=\"T_0f3f2_level0_col8\" class=\"col_heading level0 col8\" >%Good</th>\n",
       "      <th id=\"T_0f3f2_level0_col9\" class=\"col_heading level0 col9\" >%BadRate</th>\n",
       "      <th id=\"T_0f3f2_level0_col10\" class=\"col_heading level0 col10\" >WoE</th>\n",
       "      <th id=\"T_0f3f2_level0_col11\" class=\"col_heading level0 col11\" >IV</th>\n",
       "      <th id=\"T_0f3f2_level0_col12\" class=\"col_heading level0 col12\" >WoE.</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_0f3f2_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "      <td id=\"T_0f3f2_row0_col0\" class=\"data row0 col0\" >AGE</td>\n",
       "      <td id=\"T_0f3f2_row0_col1\" class=\"data row0 col1\" >1</td>\n",
       "      <td id=\"T_0f3f2_row0_col2\" class=\"data row0 col2\" >(-inf, 25.5]</td>\n",
       "      <td id=\"T_0f3f2_row0_col3\" class=\"data row0 col3\" >3871</td>\n",
       "      <td id=\"T_0f3f2_row0_col4\" class=\"data row0 col4\" >1032</td>\n",
       "      <td id=\"T_0f3f2_row0_col5\" class=\"data row0 col5\" >2839</td>\n",
       "      <td id=\"T_0f3f2_row0_col6\" class=\"data row0 col6\" >12.9033%</td>\n",
       "      <td id=\"T_0f3f2_row0_col7\" class=\"data row0 col7\" >15.5515%</td>\n",
       "      <td id=\"T_0f3f2_row0_col8\" class=\"data row0 col8\" >12.1512%</td>\n",
       "      <td id=\"T_0f3f2_row0_col9\" class=\"data row0 col9\" >26.6598%</td>\n",
       "      <td id=\"T_0f3f2_row0_col10\" class=\"data row0 col10\" >0.2467</td>\n",
       "      <td id=\"T_0f3f2_row0_col11\" class=\"data row0 col11\" >0.0084</td>\n",
       "      <td id=\"T_0f3f2_row0_col12\" class=\"data row0 col12\" >0.246700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_0f3f2_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
       "      <td id=\"T_0f3f2_row1_col0\" class=\"data row1 col0\" >AGE</td>\n",
       "      <td id=\"T_0f3f2_row1_col1\" class=\"data row1 col1\" >2</td>\n",
       "      <td id=\"T_0f3f2_row1_col2\" class=\"data row1 col2\" >(25.5, 28.5]</td>\n",
       "      <td id=\"T_0f3f2_row1_col3\" class=\"data row1 col3\" >4142</td>\n",
       "      <td id=\"T_0f3f2_row1_col4\" class=\"data row1 col4\" >852</td>\n",
       "      <td id=\"T_0f3f2_row1_col5\" class=\"data row1 col5\" >3290</td>\n",
       "      <td id=\"T_0f3f2_row1_col6\" class=\"data row1 col6\" >13.8067%</td>\n",
       "      <td id=\"T_0f3f2_row1_col7\" class=\"data row1 col7\" >12.8391%</td>\n",
       "      <td id=\"T_0f3f2_row1_col8\" class=\"data row1 col8\" >14.0815%</td>\n",
       "      <td id=\"T_0f3f2_row1_col9\" class=\"data row1 col9\" >20.5698%</td>\n",
       "      <td id=\"T_0f3f2_row1_col10\" class=\"data row1 col10\" >-0.0924</td>\n",
       "      <td id=\"T_0f3f2_row1_col11\" class=\"data row1 col11\" >0.0011</td>\n",
       "      <td id=\"T_0f3f2_row1_col12\" class=\"data row1 col12\" >-0.092400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_0f3f2_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
       "      <td id=\"T_0f3f2_row2_col0\" class=\"data row2 col0\" >AGE</td>\n",
       "      <td id=\"T_0f3f2_row2_col1\" class=\"data row2 col1\" >3</td>\n",
       "      <td id=\"T_0f3f2_row2_col2\" class=\"data row2 col2\" >(28.5, 35.5]</td>\n",
       "      <td id=\"T_0f3f2_row2_col3\" class=\"data row2 col3\" >8796</td>\n",
       "      <td id=\"T_0f3f2_row2_col4\" class=\"data row2 col4\" >1713</td>\n",
       "      <td id=\"T_0f3f2_row2_col5\" class=\"data row2 col5\" >7083</td>\n",
       "      <td id=\"T_0f3f2_row2_col6\" class=\"data row2 col6\" >29.3200%</td>\n",
       "      <td id=\"T_0f3f2_row2_col7\" class=\"data row2 col7\" >25.8137%</td>\n",
       "      <td id=\"T_0f3f2_row2_col8\" class=\"data row2 col8\" >30.3159%</td>\n",
       "      <td id=\"T_0f3f2_row2_col9\" class=\"data row2 col9\" >19.4748%</td>\n",
       "      <td id=\"T_0f3f2_row2_col10\" class=\"data row2 col10\" >-0.1608</td>\n",
       "      <td id=\"T_0f3f2_row2_col11\" class=\"data row2 col11\" >0.0072</td>\n",
       "      <td id=\"T_0f3f2_row2_col12\" class=\"data row2 col12\" >-0.160800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_0f3f2_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
       "      <td id=\"T_0f3f2_row3_col0\" class=\"data row3 col0\" >AGE</td>\n",
       "      <td id=\"T_0f3f2_row3_col1\" class=\"data row3 col1\" >4</td>\n",
       "      <td id=\"T_0f3f2_row3_col2\" class=\"data row3 col2\" >(35.5, 45.5]</td>\n",
       "      <td id=\"T_0f3f2_row3_col3\" class=\"data row3 col3\" >8522</td>\n",
       "      <td id=\"T_0f3f2_row3_col4\" class=\"data row3 col4\" >1861</td>\n",
       "      <td id=\"T_0f3f2_row3_col5\" class=\"data row3 col5\" >6661</td>\n",
       "      <td id=\"T_0f3f2_row3_col6\" class=\"data row3 col6\" >28.4067%</td>\n",
       "      <td id=\"T_0f3f2_row3_col7\" class=\"data row3 col7\" >28.0440%</td>\n",
       "      <td id=\"T_0f3f2_row3_col8\" class=\"data row3 col8\" >28.5097%</td>\n",
       "      <td id=\"T_0f3f2_row3_col9\" class=\"data row3 col9\" >21.8376%</td>\n",
       "      <td id=\"T_0f3f2_row3_col10\" class=\"data row3 col10\" >-0.0165</td>\n",
       "      <td id=\"T_0f3f2_row3_col11\" class=\"data row3 col11\" >0.0001</td>\n",
       "      <td id=\"T_0f3f2_row3_col12\" class=\"data row3 col12\" >-0.016500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_0f3f2_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
       "      <td id=\"T_0f3f2_row4_col0\" class=\"data row4 col0\" >AGE</td>\n",
       "      <td id=\"T_0f3f2_row4_col1\" class=\"data row4 col1\" >5</td>\n",
       "      <td id=\"T_0f3f2_row4_col2\" class=\"data row4 col2\" >(45.5, inf]</td>\n",
       "      <td id=\"T_0f3f2_row4_col3\" class=\"data row4 col3\" >4669</td>\n",
       "      <td id=\"T_0f3f2_row4_col4\" class=\"data row4 col4\" >1178</td>\n",
       "      <td id=\"T_0f3f2_row4_col5\" class=\"data row4 col5\" >3491</td>\n",
       "      <td id=\"T_0f3f2_row4_col6\" class=\"data row4 col6\" >15.5633%</td>\n",
       "      <td id=\"T_0f3f2_row4_col7\" class=\"data row4 col7\" >17.7517%</td>\n",
       "      <td id=\"T_0f3f2_row4_col8\" class=\"data row4 col8\" >14.9418%</td>\n",
       "      <td id=\"T_0f3f2_row4_col9\" class=\"data row4 col9\" >25.2302%</td>\n",
       "      <td id=\"T_0f3f2_row4_col10\" class=\"data row4 col10\" >0.1723</td>\n",
       "      <td id=\"T_0f3f2_row4_col11\" class=\"data row4 col11\" >0.0048</td>\n",
       "      <td id=\"T_0f3f2_row4_col12\" class=\"data row4 col12\" >0.172300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_0f3f2_level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
       "      <td id=\"T_0f3f2_row5_col0\" class=\"data row5 col0\" >Total</td>\n",
       "      <td id=\"T_0f3f2_row5_col1\" class=\"data row5 col1\" ></td>\n",
       "      <td id=\"T_0f3f2_row5_col2\" class=\"data row5 col2\" ></td>\n",
       "      <td id=\"T_0f3f2_row5_col3\" class=\"data row5 col3\" >30000</td>\n",
       "      <td id=\"T_0f3f2_row5_col4\" class=\"data row5 col4\" >6636</td>\n",
       "      <td id=\"T_0f3f2_row5_col5\" class=\"data row5 col5\" >23364</td>\n",
       "      <td id=\"T_0f3f2_row5_col6\" class=\"data row5 col6\" >100.0000%</td>\n",
       "      <td id=\"T_0f3f2_row5_col7\" class=\"data row5 col7\" >100.0000%</td>\n",
       "      <td id=\"T_0f3f2_row5_col8\" class=\"data row5 col8\" >100.0000%</td>\n",
       "      <td id=\"T_0f3f2_row5_col9\" class=\"data row5 col9\" >22.1200%</td>\n",
       "      <td id=\"T_0f3f2_row5_col10\" class=\"data row5 col10\" >0.1494</td>\n",
       "      <td id=\"T_0f3f2_row5_col11\" class=\"data row5 col11\" >0.0217</td>\n",
       "      <td id=\"T_0f3f2_row5_col12\" class=\"data row5 col12\" >0.149400</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x290587eaa70>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 按照默认的决策树方法，计算WoE 和 IV，并查看WoE的分布，设置显示的颜色\n",
    "view_woe_iv(data,'AGE',color='#02B057')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "83c82f45",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "#T_5474f_row0_col12, #T_5474f_row4_col12 {\n",
       "  width: 10em;\n",
       "  background: linear-gradient(90deg, transparent 55.6%, #007bff 55.6%, #007bff 100.0%, transparent 100.0%);\n",
       "}\n",
       "#T_5474f_row1_col12 {\n",
       "  width: 10em;\n",
       "  background: linear-gradient(90deg, #007bff 55.6%, transparent 55.6%);\n",
       "}\n",
       "#T_5474f_row2_col12 {\n",
       "  width: 10em;\n",
       "  background: linear-gradient(90deg, transparent 18.5%, #007bff 18.5%, #007bff 55.6%, transparent 55.6%);\n",
       "}\n",
       "#T_5474f_row3_col12 {\n",
       "  width: 10em;\n",
       "  background: linear-gradient(90deg, transparent 48.1%, #007bff 48.1%, #007bff 55.6%, transparent 55.6%);\n",
       "}\n",
       "#T_5474f_row5_col12 {\n",
       "  width: 10em;\n",
       "  background: linear-gradient(90deg, transparent 44.4%, #007bff 44.4%, #007bff 55.6%, transparent 55.6%);\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_5474f\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_5474f_level0_col0\" class=\"col_heading level0 col0\" >Name</th>\n",
       "      <th id=\"T_5474f_level0_col1\" class=\"col_heading level0 col1\" >No.</th>\n",
       "      <th id=\"T_5474f_level0_col2\" class=\"col_heading level0 col2\" >Bin</th>\n",
       "      <th id=\"T_5474f_level0_col3\" class=\"col_heading level0 col3\" >#Total</th>\n",
       "      <th id=\"T_5474f_level0_col4\" class=\"col_heading level0 col4\" >#Bad</th>\n",
       "      <th id=\"T_5474f_level0_col5\" class=\"col_heading level0 col5\" >#Good</th>\n",
       "      <th id=\"T_5474f_level0_col6\" class=\"col_heading level0 col6\" >%Total</th>\n",
       "      <th id=\"T_5474f_level0_col7\" class=\"col_heading level0 col7\" >%Bad</th>\n",
       "      <th id=\"T_5474f_level0_col8\" class=\"col_heading level0 col8\" >%Good</th>\n",
       "      <th id=\"T_5474f_level0_col9\" class=\"col_heading level0 col9\" >%BadRate</th>\n",
       "      <th id=\"T_5474f_level0_col10\" class=\"col_heading level0 col10\" >WoE</th>\n",
       "      <th id=\"T_5474f_level0_col11\" class=\"col_heading level0 col11\" >IV</th>\n",
       "      <th id=\"T_5474f_level0_col12\" class=\"col_heading level0 col12\" >WoE.</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_5474f_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "      <td id=\"T_5474f_row0_col0\" class=\"data row0 col0\" >AGE</td>\n",
       "      <td id=\"T_5474f_row0_col1\" class=\"data row0 col1\" >1</td>\n",
       "      <td id=\"T_5474f_row0_col2\" class=\"data row0 col2\" >(20.999, 27.0]</td>\n",
       "      <td id=\"T_5474f_row0_col3\" class=\"data row0 col3\" >6604</td>\n",
       "      <td id=\"T_5474f_row0_col4\" class=\"data row0 col4\" >1598</td>\n",
       "      <td id=\"T_5474f_row0_col5\" class=\"data row0 col5\" >5006</td>\n",
       "      <td id=\"T_5474f_row0_col6\" class=\"data row0 col6\" >22.01%</td>\n",
       "      <td id=\"T_5474f_row0_col7\" class=\"data row0 col7\" >24.08%</td>\n",
       "      <td id=\"T_5474f_row0_col8\" class=\"data row0 col8\" >21.43%</td>\n",
       "      <td id=\"T_5474f_row0_col9\" class=\"data row0 col9\" >24.20%</td>\n",
       "      <td id=\"T_5474f_row0_col10\" class=\"data row0 col10\" >0.12</td>\n",
       "      <td id=\"T_5474f_row0_col11\" class=\"data row0 col11\" >0.00</td>\n",
       "      <td id=\"T_5474f_row0_col12\" class=\"data row0 col12\" >0.120000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_5474f_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
       "      <td id=\"T_5474f_row1_col0\" class=\"data row1 col0\" >AGE</td>\n",
       "      <td id=\"T_5474f_row1_col1\" class=\"data row1 col1\" >2</td>\n",
       "      <td id=\"T_5474f_row1_col2\" class=\"data row1 col2\" >(27.0, 31.0]</td>\n",
       "      <td id=\"T_5474f_row1_col3\" class=\"data row1 col3\" >5626</td>\n",
       "      <td id=\"T_5474f_row1_col4\" class=\"data row1 col4\" >1102</td>\n",
       "      <td id=\"T_5474f_row1_col5\" class=\"data row1 col5\" >4524</td>\n",
       "      <td id=\"T_5474f_row1_col6\" class=\"data row1 col6\" >18.75%</td>\n",
       "      <td id=\"T_5474f_row1_col7\" class=\"data row1 col7\" >16.61%</td>\n",
       "      <td id=\"T_5474f_row1_col8\" class=\"data row1 col8\" >19.36%</td>\n",
       "      <td id=\"T_5474f_row1_col9\" class=\"data row1 col9\" >19.59%</td>\n",
       "      <td id=\"T_5474f_row1_col10\" class=\"data row1 col10\" >-0.15</td>\n",
       "      <td id=\"T_5474f_row1_col11\" class=\"data row1 col11\" >0.00</td>\n",
       "      <td id=\"T_5474f_row1_col12\" class=\"data row1 col12\" >-0.150000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_5474f_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
       "      <td id=\"T_5474f_row2_col0\" class=\"data row2 col0\" >AGE</td>\n",
       "      <td id=\"T_5474f_row2_col1\" class=\"data row2 col1\" >3</td>\n",
       "      <td id=\"T_5474f_row2_col2\" class=\"data row2 col2\" >(31.0, 37.0]</td>\n",
       "      <td id=\"T_5474f_row2_col3\" class=\"data row2 col3\" >6728</td>\n",
       "      <td id=\"T_5474f_row2_col4\" class=\"data row2 col4\" >1380</td>\n",
       "      <td id=\"T_5474f_row2_col5\" class=\"data row2 col5\" >5348</td>\n",
       "      <td id=\"T_5474f_row2_col6\" class=\"data row2 col6\" >22.43%</td>\n",
       "      <td id=\"T_5474f_row2_col7\" class=\"data row2 col7\" >20.80%</td>\n",
       "      <td id=\"T_5474f_row2_col8\" class=\"data row2 col8\" >22.89%</td>\n",
       "      <td id=\"T_5474f_row2_col9\" class=\"data row2 col9\" >20.51%</td>\n",
       "      <td id=\"T_5474f_row2_col10\" class=\"data row2 col10\" >-0.10</td>\n",
       "      <td id=\"T_5474f_row2_col11\" class=\"data row2 col11\" >0.00</td>\n",
       "      <td id=\"T_5474f_row2_col12\" class=\"data row2 col12\" >-0.100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_5474f_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
       "      <td id=\"T_5474f_row3_col0\" class=\"data row3 col0\" >AGE</td>\n",
       "      <td id=\"T_5474f_row3_col1\" class=\"data row3 col1\" >4</td>\n",
       "      <td id=\"T_5474f_row3_col2\" class=\"data row3 col2\" >(37.0, 43.0]</td>\n",
       "      <td id=\"T_5474f_row3_col3\" class=\"data row3 col3\" >5056</td>\n",
       "      <td id=\"T_5474f_row3_col4\" class=\"data row3 col4\" >1100</td>\n",
       "      <td id=\"T_5474f_row3_col5\" class=\"data row3 col5\" >3956</td>\n",
       "      <td id=\"T_5474f_row3_col6\" class=\"data row3 col6\" >16.85%</td>\n",
       "      <td id=\"T_5474f_row3_col7\" class=\"data row3 col7\" >16.58%</td>\n",
       "      <td id=\"T_5474f_row3_col8\" class=\"data row3 col8\" >16.93%</td>\n",
       "      <td id=\"T_5474f_row3_col9\" class=\"data row3 col9\" >21.76%</td>\n",
       "      <td id=\"T_5474f_row3_col10\" class=\"data row3 col10\" >-0.02</td>\n",
       "      <td id=\"T_5474f_row3_col11\" class=\"data row3 col11\" >0.00</td>\n",
       "      <td id=\"T_5474f_row3_col12\" class=\"data row3 col12\" >-0.020000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_5474f_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
       "      <td id=\"T_5474f_row4_col0\" class=\"data row4 col0\" >AGE</td>\n",
       "      <td id=\"T_5474f_row4_col1\" class=\"data row4 col1\" >5</td>\n",
       "      <td id=\"T_5474f_row4_col2\" class=\"data row4 col2\" >(43.0, 79.0]</td>\n",
       "      <td id=\"T_5474f_row4_col3\" class=\"data row4 col3\" >5986</td>\n",
       "      <td id=\"T_5474f_row4_col4\" class=\"data row4 col4\" >1456</td>\n",
       "      <td id=\"T_5474f_row4_col5\" class=\"data row4 col5\" >4530</td>\n",
       "      <td id=\"T_5474f_row4_col6\" class=\"data row4 col6\" >19.95%</td>\n",
       "      <td id=\"T_5474f_row4_col7\" class=\"data row4 col7\" >21.94%</td>\n",
       "      <td id=\"T_5474f_row4_col8\" class=\"data row4 col8\" >19.39%</td>\n",
       "      <td id=\"T_5474f_row4_col9\" class=\"data row4 col9\" >24.32%</td>\n",
       "      <td id=\"T_5474f_row4_col10\" class=\"data row4 col10\" >0.12</td>\n",
       "      <td id=\"T_5474f_row4_col11\" class=\"data row4 col11\" >0.00</td>\n",
       "      <td id=\"T_5474f_row4_col12\" class=\"data row4 col12\" >0.120000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_5474f_level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
       "      <td id=\"T_5474f_row5_col0\" class=\"data row5 col0\" >Total</td>\n",
       "      <td id=\"T_5474f_row5_col1\" class=\"data row5 col1\" ></td>\n",
       "      <td id=\"T_5474f_row5_col2\" class=\"data row5 col2\" ></td>\n",
       "      <td id=\"T_5474f_row5_col3\" class=\"data row5 col3\" >30000</td>\n",
       "      <td id=\"T_5474f_row5_col4\" class=\"data row5 col4\" >6636</td>\n",
       "      <td id=\"T_5474f_row5_col5\" class=\"data row5 col5\" >23364</td>\n",
       "      <td id=\"T_5474f_row5_col6\" class=\"data row5 col6\" >100.00%</td>\n",
       "      <td id=\"T_5474f_row5_col7\" class=\"data row5 col7\" >100.00%</td>\n",
       "      <td id=\"T_5474f_row5_col8\" class=\"data row5 col8\" >100.00%</td>\n",
       "      <td id=\"T_5474f_row5_col9\" class=\"data row5 col9\" >22.12%</td>\n",
       "      <td id=\"T_5474f_row5_col10\" class=\"data row5 col10\" >-0.03</td>\n",
       "      <td id=\"T_5474f_row5_col11\" class=\"data row5 col11\" >0.01</td>\n",
       "      <td id=\"T_5474f_row5_col12\" class=\"data row5 col12\" >-0.030000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x290587e8c40>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 按照等频分箱，将特征分为5箱，计算WoE 和 IV，并查看WoE的分布\n",
    "view_woe_iv(data,'AGE',qcut=5,precision=2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a2c55de0",
   "metadata": {},
   "source": [
    "## 计算WoE 并绘图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "a1e82eb3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 根据默认参数，将特征分箱，计算WoE,并展示图形\n",
    "plot_woe(data,'AGE')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "6916c130",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>No.</th>\n",
       "      <th>Bin</th>\n",
       "      <th>#Total</th>\n",
       "      <th>#Bad</th>\n",
       "      <th>#Good</th>\n",
       "      <th>%Total</th>\n",
       "      <th>%Bad</th>\n",
       "      <th>%Good</th>\n",
       "      <th>%BadRate</th>\n",
       "      <th>WoE</th>\n",
       "      <th>IV</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AGE</td>\n",
       "      <td>1</td>\n",
       "      <td>(-inf, 25.5]</td>\n",
       "      <td>3871</td>\n",
       "      <td>1032</td>\n",
       "      <td>2839</td>\n",
       "      <td>12.90%</td>\n",
       "      <td>15.55%</td>\n",
       "      <td>12.15%</td>\n",
       "      <td>26.66%</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AGE</td>\n",
       "      <td>2</td>\n",
       "      <td>(25.5, 28.5]</td>\n",
       "      <td>4142</td>\n",
       "      <td>852</td>\n",
       "      <td>3290</td>\n",
       "      <td>13.81%</td>\n",
       "      <td>12.84%</td>\n",
       "      <td>14.08%</td>\n",
       "      <td>20.57%</td>\n",
       "      <td>-0.09</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AGE</td>\n",
       "      <td>3</td>\n",
       "      <td>(28.5, 35.5]</td>\n",
       "      <td>8796</td>\n",
       "      <td>1713</td>\n",
       "      <td>7083</td>\n",
       "      <td>29.32%</td>\n",
       "      <td>25.81%</td>\n",
       "      <td>30.32%</td>\n",
       "      <td>19.47%</td>\n",
       "      <td>-0.16</td>\n",
       "      <td>0.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>AGE</td>\n",
       "      <td>4</td>\n",
       "      <td>(35.5, 45.5]</td>\n",
       "      <td>8522</td>\n",
       "      <td>1861</td>\n",
       "      <td>6661</td>\n",
       "      <td>28.41%</td>\n",
       "      <td>28.04%</td>\n",
       "      <td>28.51%</td>\n",
       "      <td>21.84%</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>AGE</td>\n",
       "      <td>5</td>\n",
       "      <td>(45.5, inf]</td>\n",
       "      <td>4669</td>\n",
       "      <td>1178</td>\n",
       "      <td>3491</td>\n",
       "      <td>15.56%</td>\n",
       "      <td>17.75%</td>\n",
       "      <td>14.94%</td>\n",
       "      <td>25.23%</td>\n",
       "      <td>0.17</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Total</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>30000</td>\n",
       "      <td>6636</td>\n",
       "      <td>23364</td>\n",
       "      <td>100.00%</td>\n",
       "      <td>100.00%</td>\n",
       "      <td>100.00%</td>\n",
       "      <td>22.12%</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.02</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Name No.           Bin  #Total  #Bad  #Good   %Total     %Bad    %Good  \\\n",
       "0    AGE   1  (-inf, 25.5]    3871  1032   2839   12.90%   15.55%   12.15%   \n",
       "1    AGE   2  (25.5, 28.5]    4142   852   3290   13.81%   12.84%   14.08%   \n",
       "2    AGE   3  (28.5, 35.5]    8796  1713   7083   29.32%   25.81%   30.32%   \n",
       "3    AGE   4  (35.5, 45.5]    8522  1861   6661   28.41%   28.04%   28.51%   \n",
       "4    AGE   5   (45.5, inf]    4669  1178   3491   15.56%   17.75%   14.94%   \n",
       "5  Total                     30000  6636  23364  100.00%  100.00%  100.00%   \n",
       "\n",
       "  %BadRate    WoE    IV  \n",
       "0   26.66%   0.25  0.01  \n",
       "1   20.57%  -0.09  0.00  \n",
       "2   19.47%  -0.16  0.01  \n",
       "3   21.84%  -0.02  0.00  \n",
       "4   25.23%   0.17  0.00  \n",
       "5   22.12%   0.15  0.02  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 根据默认参数，将特征分箱，并计算WoE,并展示图形 和 数据\n",
    "plot_woe(data,'AGE',return_data =True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "0bfbfdfd",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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tjO7pQpMRCSFEkxg4cKD6wzIjI4OtW7eya9cu4ORDWN++fTEYDNjtdjZs2EBmZqZ6fnx8PFFRUQBuD4hAjSk5p76u/sBX3aRJk2okOJ8PVSMxVaruoz7vvfce48aNY+PGjeTl5alTaqqMGzeO//73v2edpBccHFzrKM2ZCA0NdXt96vteWFhYo+zUoOp0v6DPVmN9j5yaHG4ymdS/V01hgMrRnZ07d/L5559jtVrVPJ0qnTt35qeffqozF0OIC+ndd9+lf//+9OzZk9mzZ9OlSxe0Wi1btmxh37599OjRA6icVnnFFVcwceJE5s+fj8Ph4IEHHmDQoEH07NkTgH/+85+MGzeOK6+8kqFDh/Ldd9+xYsUK/ve//6lttG3blkmTJjFnzhyKi4t5+umnT9vHxp7atG7dOm644QYefvhhbr31VvX3i9FoVK/z0ksv0bNnT1q1aoXVauWHH37g008/VaealpWV8Z///IebbrqJiIgIcnNzeeedd0hPT+e2224DKkeE77vvPmbNmkV0dDQxMTHqEtVVdU5dpjo3NxeoDLr8/f0b7Z7PBwkkhBBN4tQ8iZdeekl9EKsKJDw8POjVqxd//PEHv/76q1sgUf0T31N/uZz6yfepr0/du+FCKisrc5t25ePjo/4Crk90dDSJiYkcOnSIzZs3c/DgQXbt2sU333yDw+Fg2bJljBgxgsmTJ59Vv+pareVMZGdnu03bOvV9r/qFWD3YqcrtqHLw4MFz7kdtGut7xGAwuL2uvhN5dXq9nk8++YTXXnuNP/74g/3797N//35WrlxJQUEBu3fv5sknn3SbkicuXc19t+lWrVqxfft2XnzxRZ566inS0tIwmUx07NiRxx9/XE2c1mg0fPPNNzz44IMMHDjQbfnXKjfffDNvvPEGc+fO5eGHHyYuLo6PP/5YnQqk1WpZuXIlU6ZMoXfv3sTGxvLmm2+6rWh3ISxevJjy8nJeeuklXnrpJbW8akotVP68fuCBB0hLS8PDw4P27dvz2WefqatU6XQ69u3bx+LFi8nNzSUoKIhevXrx66+/uuVazJkzB71ezx133EFFRQV9+vTh559/btLfRY1GEUKIJtKyZUsFUABFo9EogGIwGJTS0lK1zpNPPul2vOrrww8/VOvk5uYqOp1OPTZu3Di364wbN049ptPplNzcXPVY9TYnTZp0zvdUX3tFRUXKqFGj3Oo89dRTdZ7/8ccfq+U7duxQnE5njevddNNNav3p06er5S+88IJaHhISUmtfZ82apdaJiYmp855iYmLUerNmzVLL161b59bfe+65Rz1ms9mUTp06qceioqLUY2+88YbbeYcOHVIURVEsFosyaNAgt2Pr1q1Tz0tLS3M79v3339foa3Jycq3nO51OJTAwUC3v27ev4nA41POeeOIJt/N27NihHqvr/4miKMqkSZPUY4MGDVLL9+3bp5SVldXo37x589T6nTt3rvM9FxefiooKZe/evUpFRUVTd0WIRnO672sZkRBCNJmBAwfy2WefASenuHTv3t3t0/FBgwbx8ssv15gCU31EIigoiLvuukvd92DZsmUUFhbWWLUJ4M4776xzCk9dqzYBTJ069YyTY6vas1gsHDhwgO+++85tM71evXrx7LPPNqit8ePHU1RUxJAhQ4iKiiIwMJDDhw+7TXGqPgRefbpUTk4OkydPpmPHjmg0GqZNm3ZeEigXLlxITk4OXbp04ccff3TbW6H6XhynrhrVv39/Bg0axJ9//qku/1ubkJAQdaobVK6C8tdff2EwGBg8eHC9IztarZZHH31Ufb8TExMZMGAAw4YNY9++fW6rOQ0ZMkRdA/5svf7663z66adce+21xMXFERYWRn5+Pp988olap7lPWRBCiNO6oGGNEEJU8/7777t92gsojz/+uFud4uJit9EGQAkPD6/RVmlpqTJw4MAa7VX/6t+/v1JSUuJ2Xn31q38lJyc36J4a2t5tt92mFBYW1nt+9U+/27VrV297gYGBSkpKilo/IyND8fT0rLVuTk6OoiiNPyIxePDgWq/Xo0cPpby83K3Nq6++uta6119/fZ0jEoqiKLfcckut582ZM0dRlLpHJBRFURwOh3LbbbfV+z526NBBSU9Pb9D/E0Wpe0Ti3nvvrfc6Wq1WWblyZZ3vubj4yIiEuBSd7vtaVm0SQjSZ2nYHrT7SAJU5BFdeeWW9daByjn9CQgIffPABQ4YMITAwEL1eT0BAAIMGDeL//b//x/r16y/4ZnRarRYPDw8iIyPp27cvDz74INu2bWPZsmVntBnZSy+9xH333UePHj0IDw/HYDDg6elJ+/bteeCBB9i2bZu6AghAeHg43333Hf3792+U/IeG+OCDD5g3bx4dOnTAZDIRERHBww8/zM8//1xjBOTbb7/lH//4ByEhIZhMJrp06cIHH3ygblRYl4ULFzJp0iTCwsLOOLFcp9OxbNkyvvzyS66//npCQ0PR6/X4+fnRp08f5syZw5YtW85pU6sqU6ZMYcaMGQwcOJDo6GjMZjNGo5Ho6Ghuu+02fvnlF26++eZzvo4QQjQljaKcxTqEQgghhBBCZbFYSE5OJi4ursl3cReisZzu+1pGJIQQQgghhBBnTAIJIYQQQgghxBmTQEIIIYQQQghxxiSQEEIIIYQQQpwxCSSEEEIIIcR5MXv2bLp169bU3RDniWxIJ4QQQghxPj13AZednlV6xqfcddddLF68WH0dGBhIr169ePXVV+nSpUtj9u6CSUpKYsaMGfzyyy84HA46duzI8uXLadmypVonMTGRp59+mk2bNqHT6ejWrRtr1qxRl6u+6aab2LFjB9nZ2QQEBDB06FBeeeUVtyWiFUXhtdde4/333+fo0aMEBwfzwAMP8PTTT1/we24KMiIhhBBCCHGZGzFiBBkZGWRkZJCQkIBer+eGG25o6m6dlcOHDzNgwADat2/P+vXr2blzJ88++6zb8qWJiYmMGDGCYcOGsXnzZrZs2cL06dPd9qcZMmQIy5YtY//+/SxfvpzDhw8zduxYt2s9/PDDfPDBB8ydO5d9+/bx7bff0rt37wt2r01N9pEQQgghhDhH9a63fxGMSBQWFvL111+rZb/99htXX3012dnZhISEADBjxgxWrlxJWloa4eHhTJw4kZkzZ2IwGNTzXn75ZV5//XXKy8sZN24cISEhrF69mh07dpzrnTXYhAkTMBgMfPrpp3XWueqqq7juuuv497//3eB2v/32W26++WasVisGg4GkpCS6dOnC7t27adeuXWN0vdmRfSSEEEIIIUSDlZaW8tlnn9G6dWuCgoLUch8fHxYtWsTevXt54403WLhwIa+//rp6fNmyZcyePZsXX3yRrVu3EhERwbvvvnva63l7e9f7dd999zW47y6Xi++//562bdsyfPhwQkND6dOnj1uQlJ2dzaZNmwgNDaVfv36EhYUxaNAgfvvttzrbzc/PZ8mSJfTr108NnL777jvi4+NZtWoVcXFxxMbG8o9//IP8/PwG9/diJyMSQgghhBDn6GIfkfjss8/UfpeVlREREcGqVavo3r17nefNnTuXpUuXsnXrVgD69evHlVdeyTvvvKPWueqqq7BYLPWOSBw6dKje/vn6+hIaGtqge8nMzCQiIgJPT09eeOEFhgwZwurVq/nXv/7FunXrGDRoEBs3bqRv374EBgYyd+5cunXrxieffMK7777L7t27adOmjdrejBkzePvttykvL+eqq65i1apVanB13333sWjRIrp168acOXNwOp08+uijBAQE8PPPPzeov83d6UYkJNlaCCGEEOIyN2TIEN577z0ACgoKePfddxk5ciSbN28mJiYGgC+++II333yTw4cPU1paisPhwNfXV20jKSmpxuhB3759WbduXb3Xbt26daPdh8vlAmD06NE8+uijAHTr1o0//viDBQsWMGjQILXOvffey+TJkwG48sorSUhI4KOPPuKll15S2/vnP//JlClTOHr0KM899xx33nknq1atQqPR4HK5sFqtfPLJJ7Rt2xaADz/8kB49erB///5LdrpTdTK1SQghhBDiMufl5UXr1q1p3bo1vXr14oMPPqCsrIyFCxcClcnJEydO5Prrr2fVqlVs376dp59+GpvNds7XbsypTcHBwej1ejp27OhW3qFDB1JTUwGIiIgAqLdO9fbatm3Lddddx9KlS/nhhx/YuHGj2o5er1eDiKo2gBrtXKpkREIIIYQQQrjRaDRotVoqKioA+OOPP4iJiXFb1vTo0aNu53To0IFNmzZx5513qmVVD931OV0idvVRj9MxGo306tWL/fv3u5UfOHBAHVmJjY0lMjKy1jojR46ss+2qkQyr1QpA//79cTgcHD58mFatWqltAOq1LnUSSAghxGXi3XffZdq0afTu3ZtNmzbVWic7O5t58+bx/fffk5ycjMPhoEWLFlx99dVMmTKFAQMGqHUXLVqkTguoTWJiIldddVWj34cQovFZrVYyMzOByqlNb7/9NqWlpdx4440AtGnThtTUVJYuXUqvXr34/vvvWblypVsbDz/8MHfddRc9e/akf//+LFmyhD179hAfH1/vtRtzahNUTkcaP348AwcOVHMkvvvuO9avXw9UBkn//Oc/mTVrFl27dqVbt24sXryYffv28dVXXwGwadMmtmzZwoABAwgICODw4cM8++yztGrVir59+wIwdOhQunfvzt133838+fNxuVxMmzaN6667zm2U4lImgYQQQlwmlixZQmxsLJs3b+bQoUM1fnlv3ryZUaNGUVJSwoQJE7jvvvswmUwkJyfz9ddfs2jRIn755RcGDhzodt7zzz9PXFxcjes19sOBEOL8Wb16tTrlx8fHh/bt2/Pll18yePBgoHJztkcffZTp06djtVoZNWoUzz77LLNnz1bbGD9+PIcPH+aJJ57AYrFw6623cv/997NmzZoLei+33HILCxYs4KWXXuKhhx6iXbt2LF++3O2DkEceeQSLxcKjjz5Kfn4+Xbt2Ze3aterIgqenJytWrGDWrFlq8vmIESN45plnMJlMAGi1Wr777jsefPBBBg4ciJeXFyNHjuS11167oPfblGTVJiGEuAwkJycTHx/PihUruPfee5k2bRqzZs1SjxcUFNCpUycURWHdunW0b9/e7XxFUVi6dKk6fxpOjkhs2bKFnj17XtD7EaK5Od3qNkJcjGQfCSGEECxZsoSAgABGjRrF2LFjWbJkidvxBQsWkJGRwfz582sEEVA5FeD2229XgwghhBBCAgkhhLgMLFmyhDFjxmA0Grn99ts5ePAgW7ZsUY9/9913eHh4MGbMmDNuu6ioiNzcXLevvLy8xuy+EEKIZkhyJIQQ4hK3bds29u3bx1tvvQXAgAEDaNGiBUuWLFFHGPbt20e7du3UHVurlJSUqCuUAHh4eODl5eVWZ+jQoTWuaTKZsFgsjX0rQgghmhEJJIQQ4hK3ZMkSwsLCGDJkCFA5TWn8+PF89tlnvPbaa+h0OoqLi/H2rrn77h133ME333yjvp42bRpvv/22W5133nmnxgolOp3uPNyJEEKI5kQCCSGEuIQ5nU6WLl3KkCFDSE5OVsv79OnDa6+9RkJCAsOGDcPHx4fS0tIa5z///PNMnz4dgOuuu67Wa/Tu3VuSrYUQ4jIkgYQQQlzCfv75ZzIyMli6dClLly6tcXzJkiUMGzaM9u3b89dff2G3292mN3Xp0uVCdlcIIcRFRAIJIYS4hC1ZsoTQ0FDeeeedGsdWrFjBypUrWbBgATfccAMbN25k5cqVjBs3rgl6KoQQ4mIjgYQQQlyiKioqWLFiBbfddhtjx46tcTwyMpL//ve/fPvtt9x///289dZbPProo3Tr1q1GzoNsOSSEEOJUEkgIIcQl6ttvv6WkpISbbrqp1uNXXXUVISEhLFmyhPHjx7Ny5UpuvPFGunbtyoQJE+jVqxcGg4Fjx47x5ZdfAtCyZcsa7fz444/s27evRnm/fv2Ij49v3JsSQgjRbEggIYQQl6glS5ZgNpvrTJLWarWMGjWKJUuWkJeXR9++fdm9ezfz5s3j+++/54svvsDlchEVFcWAAQN4//33ufrqq2u0M3PmzFrb//jjjyWQEEKIS5hsSCeEEJeob7/9loqKCjw9Peus8/HHH2Oz2QgKCgIgPDycV199lT179lBeXo7FYuHw4cMsXry4RhBx1113oShKnV933XXX+bw9IS5ZLpeLlJQUdu3aRUpKCi6X67xda8GCBfj4+OBwONSy0tJSDAYDgwcPdqu7fv16NBoNhw8fPm27KSkpaDSaWr82btzY2Lehys/PZ+LEifj6+uLv78+UKVNqXZGuuvfff5/Bgwfj6+uLRqOhsLDQ7XjVfdf2VX1jz8uRjEgIIYQQQjQTSUlJrF69muLiYrXM19eXESNG0KFDh0a/3pAhQygtLWXr1q1cddVVAPz666+Eh4ezadMmLBYLZrMZgHXr1tGyZUtatWrV4Pb/97//0alTJ7eyqg8uzoeJEyeSkZHB2rVrsdvtTJ48malTp/L555/XeU55eTkjRoxgxIgRPPXUUzWO9+vXj4yMDLeyZ599loSEhMt+6WsZkRBCCCGEaAaSkpJYtmyZWxABUFxczLJly0hKSmr0a7Zr146IiAjWr1+vlq1fv57Ro0cTFxfnNnqwfv16dWNLq9XKQw89RGhoKGazmQEDBtT66XxQUBDh4eFuX9WXmG5MVUHYBx98QJ8+fRgwYABvvfUWS5cu5fjx43We98gjj/Dkk0+qgdSpjEajW/+DgoL45ptvmDx5MhqN5rzcy8VCAgkhhBBCiCbmcrlYvXp1vXVWr159XqY5DRkyhHXr1qmv161bx+DBgxk0aJBaXlFRwaZNm9RA4oknnmD58uUsXryYP//8k9atWzN8+HDy8/PPqS+dOnXC29u7zq+RI0fWeW5iYiL+/v5uowRDhw5Fq9WyadOmc+pXdd9++y15eXlMnjy50dq8WMnUJiGEEEKIWhQfPEjWugQKd+/Ckp2NwccH37btiP3b3/GMilLr7XtzPtk7/8Lnjrso02qw63QAaI1Glqxec9o5+gAOh4OKior6+1NczJw5c9BrtYACaCr/02ih2ifj3t7e3DV+PLbCAnC60Hl5Yg4OQXOiX1C5pHN52jH0Xt4MGTKERx55RO3D9u3bGTRoEHa7nQULFgCVD+lWq5UhQ4ZQVlbGe++9x6JFi9QH+4ULF7J27Vo+/PBD/vnPf6rX6devH1qt++fW9b0fP/zwA3a7vc7jHh4edR7LzMwkNDTUrUyv1xMYGEhmZmad552pDz/8kOHDh9OiRYtGa/NiJYGEEEIIIUQtjq1cTvG+JEL69ccrJhZbYQHpP3zPtscfpfvLc/CKiVHravQGDL6+GIODMRuNlYVaHaWlpZSUlDRanywWy+krKQrWnBwMfn5oDQZshQVY8/IwV3vIthcXo7hcGP39GTx4MGVlZWzZsoWCggLatm1LSEgIgwYNYvLkyVgsFtavX098fDwtW7Zk586d2O12+vfvr7ZnMBjo3bt3jelXX3zxxRnldsRUe0+bo7S0NNasWcOyZcuauivNggQSQgghhBC1aHHTaHwe/T+01eb0h/S/mq2PPEjqiq/o8Oj/qeUanRad2YzByxvDieRkqBwdaIiGjEhA5Sfyer3745vidFaOTGgrRxy8zGZ0Hh6YQ0Iq+6bVYs3Pc6tvy8/DFBKKRquldevWtGjRgnXr1lFQUMCgQYOAyk0ro6Oj+eOPP1i3bh3XXHNNg+6luujoaFq3bt3g+p06deLo0aN1Hr/66qv58ccfaz0WHh5Odna2W5nD4SA/P5/w8PAG96E+H3/8MUFBQXXuz3O5kUCikTgcDrZv305YWFiNITwhhBBCXIS8fSjJyqpRbAgPpzD5CGlpaQCUl5WjuBQcDgc2q9XtOaChyyC7nE7eefttSsvL66zj4+PDAw88UOM5w5aRAVoNxrDKh2VHQX5lsGCzVbatKCgul/raUVAABgOK0aiWDRo0iJ9//pnCwkIee+wxtXzAgAGsWrWKzZs3c88992Cz2YiOjsZoNPLLL78wYcIEAOx2O1u2bGH69OnYbDb1fLvdrv69Ib7++uvTTm2qq70ePXpQWFjIxo0b6d69OwBr167F5XJx5ZVXYrPZUJTK/0+enp5nnCitKAoff/wxd95553lLGL/YSCDRSLZv307v3r2buhtCCCGEOM++HD6U5OISnoiOBuDJ7t0Y0a0b9vx87EYDLq2WYpud3IoKGpoaHWAyMbB7d3747bc667Rp04bdu3ej1WjQADqNBl+jkSCziTyLldysyk/jfY0Gwjw8OZq9F7vLRbinJwoK+3fuxKjVEuvjw9HSEqyZJ4Ok+Ph4vvrqKxwOB0FBQezcuROA2NhY5syZg81mIzQ0VC0fM2YMjz/+OIWFhYSHh/PJJ59QUlJC79692blzp7pK0pYtW8jLy3O7Dx8fH0wmUwPfGXfl5eU12quub9++3HXXXTz11FM4HA6ef/55hg0bRm5uLrm5uWRnZ/PAAw+wePFideQlMzOTzMxMDh06BMCuXbvw8fGhZcuWBAYGqm3//PPPJCcn849//OOs+n4p0iiKojR1Jy4FqampxMTEsHnzZiIiIpq6O0IIIYQ4D0q2bCL7k8WE/O3v+PbtB0Det1+jmMxou11Jy+gWGJwuXGWlaExmjKGhbonQtVGcTmwZx9H7B3AoPZ21P/1ESbWEZB8fH6677jratWsHgD07G5flxDQojQadlze6gICTn7ArCva8XFwnRjc0ej36kBC0BiP27OzK19UekKFyA7l27drRrl07NVgAOHr0KG3btqVt27bs2rVLLbdYLDz11FMsW7aMkpISevTowZw5c9QVk6raq82nn37KuHHjTvdWn5X8/HweeeQRvv/+e7RaLbfccgvz5s1Tp5gdOnSITp06sWbNGoYNGwbA7Nmzee6552q09fHHH7uNKP3tb3/j6NGj/P777+el782RxWIhOTmZuLg4dT+R6iSQaCRpaWlER0dz7NgxyeIXQgghLkHlaWn8OeNxvKJb0u0/L7mtgnTqA5c1Px9bfh7msHAMPj71tluRlYXLZsWzRTQajQZrYSGH9+zG7uGJX2AgLVu2dJvO5LRaUZxOXA4HjpJitHoDppAQNKdMeXLZbCguF1qjEY1Wi72sFGtWNp4xMaAoWHKycVmtaE0mzCGhaPWX/kQVm83Gzp076dKlC8aqpHhRp9MFEjKZXwghhBDiNGwFBex64Xn0np50fGKGWxBRG6O/P6DBWVF3zgOA01KBo6QEU1CwOqKg1WqJDgujc8eOxMbG1siJ0JlM6D09Mfr64hERidNqxZJdM5dDazSiM5vRaLWVORK5eRgDA9HqdFgyM9FoNHiER6DRaLBkNd7yqOLyIYGEEEIIIUQ9HGVl7Pr3bBxlZVwxczamwKDTnqPRatHotCjO+rMkrHl56DzMaA0GXHY7Lru9chUmKhOwXfUkHlddR+/liaO0DKWezepsRUWgAYOfHy67HaelAlNQMDqzGVNQMM6KitNeS4hTXfpjWEIIIYQQZ8lls7H7xX9Tfvw4XWb/G6/olg06T3E5UZyu045cuOwOFIedsqMpNY5ZMo6DVodPfHz911IUoHJlplOnNwG4HA5sBfl4hIWj0WhwnQhUqvpW9afidIKsRiTOgAQSQgghhBC1UJxO9s59leL9++n01NP4tW9fo47LZsPldIDG/QHeml8AKOg8PU+2pyi47HY0Wq2aj2AODa0xkuCsqMBeVIgpOBit4eQ8fpfDUSOPQXE6cZSWodEb6sxxsObloTN7oPfyAk4GDi67HZ3JpI5EnC7oEeJUEkgIIYQQQtTi8KKPyNuymaBevXGUlJC1fp3b8bDBQ7AVFrDtsUcIum4Yjiu6YCsuxpWfj7O8DJ2nl/rwDqA4HJSnHkXv44tHWBgA+mqBhsrlwg7ozB7oqiW4VmRkoNXr0JrMaPQ6FLsDe0kJisOBuY4N15wWC47SEjyrjaRoDQa0JjOW7CwMvr7Yi4vRms1uG+8J0RASSAghhBBC1KI0ORmAvC2byduyucbxsMFD0Ht5EdizF8X7kjDEtcJeWIDO7IExKAijf8AZb3pWH4OvD46SUuxFheo0Jp3ZjCEsDL2HR436iqJgyc3B4OeH7pQVijzCwrDkZFeOVphMmEPDGq2f4vIhy782Eln+VQghhLh8nW6ZTNE8yPKvZ0aWfxVCCCGEEEI0OgkkhBBCCCGEEGdMAgkhhBBCiGZCURQcFeXYS0pwVJRzsc9Anz17Nt26dWvqbojzRAIJIYQQQohmwF5aStnRFMqPHSPnj99IX/UdmQlrsRUXn9fr3nXXXWg0GvUrKCiIESNGsHPnzka/1uzZs9Xr6HQ6oqOjmTp1Kvn5+Wfc55tvvrnR+3em/vrrL26//Xaio6Px8PCgQ4cOvPHGG251VqxYwXXXXUdISAi+vr707duXNWvW1NuuxWLhrrvu4oorrkCv19d6rw1pd8mSJURHRxMQEMBjjz3mdiwlJYW2bdtSfA7fXxJICCGEEEI0MXtpKZbMDPK3/8nuF1/g4IL3SPl8CQfeeZutD00jc93P5/X6I0aMICMjg4yMDBISEtDr9dxwww3n5VqdOnUiIyOD1NRUPv74Y1avXs39999/Xq51vm3bto3Q0FA+++wz9uzZw9NPP81TTz3F22+/rdbZsGED1113HT/88APbtm1jyJAh3HjjjWzfvr3Odp1OJx4eHjz00EMMHTq01jqnazc3N5d//OMfzJ07l59++onPPvuMVatWqec/8MADvPzyy/j6+p71/UsgIYQQQgjRhBRFwZqbQ8GunSR/shh7UZHbcXtREfvfnE9O4h/nrQ8mk4nw8HDCw8Pp1q0bTz75JMeOHSMnJ0etM2PGDNq2bYunpyfx8fE8++yz2E9sZlfl5ZdfJiwsDB8fH6ZMmYLFYqlxLb1eT3h4OFFRUQwdOpTbbruNtWvXqsedTidTpkwhLi4ODw8P2rVr5/Yp/+zZs1m8eDHffPONOrqxfv16AI4dO8a4cePw9/cnMDCQ0aNHk5KS0rhvVjV33303b7zxBoMGDSI+Pp6///3vTJ48mRUrVqh15s+fzxNPPEGvXr1o06YNL774Im3atOG7776rs10vLy/ee+897rnnHsLr2CPkdO0eOXIEPz8/xo8fT69evRgyZAhJSUkA/Pe//8VgMDBmzJhzun8JJIQQQgghLiDF5cJptWIvLcVWUEBFZgYum420b76u97xDHy5EcTrPe/9KS0v57LPPaN26NUFBQWq5j48PixYtYu/evbzxxhssXLiQ119/XT2+bNkyZs+ezYsvvsjWrVuJiIjg3XffrfdaKSkprFmzxm0pVpfLRYsWLfjyyy/Zu3cvM2fO5F//+hfLli0D4PHHH2fcuHFuoyj9+vXDbrczfPhwfHx8+PXXX/n999/x9vZmxIgR2Gy2Ovvg7e1d79d99913Ru9fUVERgYGBdR53uVyUlJTUW+dsnNpumzZtKC8vZ/v27eTn57Nlyxa6dOlCQUEBzz77rNuoydmSDemEEEJcMooPHiRrXQKFu3dhyc7G4OODb9t2xP7t73hGRan1Mn5aQ9Yv6ylPT8NRVoYpMBC/zlcQO35CgzbmOvrVMvK2bMaSmYmjogJzcDCBPXrScuw4jH5+bnUVl4tj36wkY/WPWAsK8IyMpOWtYwm9epBbvdxNGzny6WJsBQX4dexE2/sfwBQY5FZn94svYPT3p+0D08/hXRIXguJy4bLb2f7UE9gLC6nMmVagjuRpl8OBs6ys3jZteXn8cfek0+5AbfQPoMfceWfU31WrVuHt7Q1AWVkZERERrFq1Cq325GfOzzzzjPr32NhYHn/8cZYuXcoTTzwBVH5CPmXKFKZMmQLACy+8wP/+978aoxK7du3C29sbp9OpHps372R/DQYDzz33nPo6Li6OxMREli1bxrhx4/D29sbDwwOr1er2af1nn32Gy+Xigw8+UDcC/Pjjj/H392f9+vUMGzas1nvfsWNHve+Nr68v2dnZZGZmYrfb8fT0pGXLlnhV27W8yh9//MEXX3zBggUL1ClGnp6etGjRQq0/d+5ciouLadu2LVu3bnW7Ttu2bevtS33mzp1LaWkp48aNAyAgIIDFixdz5513UlFRwZ133snw4cOZMmUK06dPJzk5mZtuugm73c7s2bMZO3bsGV9TAomL2XPeTd0Dcb7MKm3qHghxUTq2cjnF+5II6dcfr5hYbIUFpP/wPdsef5TuL8/BKyYGgNLkI5jDwgjq3Ru9lzeW7Cwy1v5E/tYt9Hj9jRoP8KcqPXwY79h4Qgdcjc7Dg/K0tMrzt22lx7w30FXbuCl5yaccW7GciOuG4dO6DbmbN5E07zVAQ+jVAwGoyMxk79xXCR1wNb7t2pH23Xfsf+tNusw6+TCVv/1PCvfsofe7Cxr/jRNnxVlRQUVmJhWZGZTk5GAPD6ciKxOHoqA4HADY8vNrTFU6F47zlHg9ZMgQ3nvvPQAKCgp49913GTlyJJs3bybmxL+bL774gjfffJPDhw9TWlqKw+Fwm1+flJRU49P7vn37sm7dOreydu3a8e2332KxWPjss8/YsWMHDz74oFudd955h48++ojU1FQqKiqw2WynXf3pr7/+4tChQ/j4+LiVWywWDh8+XOd5rVu3rrfd/Px8kpOTiYmJwcvLi6ysLA4cOEDnzp0xVAvqdu/ezejRo3nwwQe58cYb8fb2RqPRkJmZyYEDB+jUqRNfffUVzz33HAsWLCA2Npa4uDj1/HPZBf3zzz/nueee45tvviE0NFQtv+WWW7jlllvU17/88gs7d+7krbfeonXr1vz3v/8lPDyc3r17M3DgQLdzG0ICCSGEEJeMFjeNxufR/3P7xDak/9VsfeRBUld8RYdH/w+ANvfWTOwM7nMVfz7+GFnr1tHy1vo/mes046kaZb7t2rP31ZfJ27JZDRCseXmkffsNkSOvp83Uyges8OuG8dczT3Fk8SJC+vVHo9NRsGM7pqBg2j30CBqNBs8W0fw18xlcNhtaoxHF6eTwRx8SM258jREPcX45KsqpyMjAkpFBRWYGFcePV/6ZkYmt4ORKQ5qgIHzuuAuXxYKi06nlhlMeaisra078oVFfO63W045IAOh9fRs0InGmvLy83B6oP/jgA/z8/Fi4cCEvvPACiYmJTJw4keeee47hw4fj5+fH0qVLee211874WkajUb3Wyy+/zKhRo3juuef497//DcDSpUt5/PHHee211+jbty8+Pj7MmTOHTZs21dtuaWkpPXr0YMmSJTWOhYSE1Hle1UhMXUaNGsWcOXMIDg4GICYmhqKiInJzc4mIiABg7969XHvttUydOpX//Oc/bufHxsayfft2Fi9ezMMPP8yXX35Jx44dcTqdboHI2Vq6dCn/+Mc/+PLLL+tMzAawWq088MADfPrppxw6dAiHw8GgQZUjo23btmXTpk3ceOONZ3RtCSSEEEJcMvzad6hR5hkZiVd0S8rT0uo91xxS+UmcowEPc7WeH1rz/NzNm1AcDiJHXq+WaTQaIkeMJGneaxTv349fx464bDb0Xl7qg6Xe2xsUBeeJQCL9h1UoLhdRo87PKjqXO0d5ORUZx6nIyDjxdeLvmRnYCwvPuD2NTofWYERjMNDluRfQGgxoDQY0BgPaakFGdbbiYrY+NK3e0QtTcDB9FixEU0cbjUmj0aDVaqmoqAAqp+zExMTw9NNPq3WOHj3qdk6HDh3YtGkTd955p1q2cePG017rmWee4ZprruH+++8nMjKS33//nX79+vHAAw+odU4dUTAajThPyRfp3r07X3zxBaGhoaddicjpdKrnb9u2Tb3fU7lcLlJSUtxGOTQaDb6+vpSd+Le+Z88errnmGiZNmlQjiKhq48cff+SFF15g6dKljBo1iuTkZEpKStixYwd6vR4fHx+ioqLQ68/s0fy///0vd999t9pufV544QVGjBhB9+7d2b59O44To2YAdru9xvvZEBJICCGEuKQpioKtqBCv6JY1jtmLi1FcLqy5ORxd9gUA/l26NLhdR0kJitNJecZxkj9dDFot/p2vUOuUHjmC1mzGs0W027k+rSvnQZcmH8GvY0d8Wrfh8KKPyP71F3zatiP1qy/xiIjA4O2NraiIo18spf0jj6E9w4cMcZKjrNQ9SKj2ZS8+86lHBn9/PMIj8IiMxCM8An1kJIU+vni2iMajlrnzp2P09SV+0mT2vzm/zjqt7v7HeQsirFYrmZmZQOXUprfffpvS0lL1E+o2bdqQmprK0qVL6dWrF99//z0rV650a+Phhx/mrrvuomfPnvTv358lS5awZ88e4uPj671237596dKlCy+++CJvv/02bdq04ZNPPmHNmjXExcXx6aefsmXLFrdpQLGxsaxZs4b9+/cTFBSEn58fEydOZM6cOYwePZrnn3+eFi1acPToUVasWMETTzxBixYt1PP37Nnj1ofIyEgiIyNr9M1ms1FcXFxj5MBgMGCxWNi9ezfXXHMNw4cP57HHHlPfQ51Op46CvPPOO8yePZv58+fTp08fMjMzsVgsBAYGEhISgtVqZc6cOfz8888kJiaqHyjs3bsXm81Gfn6+GnQA6hSvzz//nEmTJvHGG2+o7QJ4eHjgd8rI5d69e/niiy/UvI327duj1Wr58MMPCQ8PZ9++ffTq1ave/0+1kZ9IQgghLmnZv6zHlpdH7IS/1TiW+I/JKCeWr9T7+ND6H1MJ7HZlg9q1FxaSePck9bUpKJgOjz2OZ7WHFVtBPkY//xpzn40nVlWx5ucB4NexI1HX33AidwL03j50fGIGAClLPsWnbTuCep75L/nLjb2k5GSwkJnhNsLgKCk54/aMAYF4RIS7BQzmiAg8IiLQe3i61bVYLBQnJ5/Tg374kGvQmc0c+uB9bNU2aDMFB9Pq7n8Q0rffWbd9OqtXr1an6fj4+NC+fXu+/PJLBg8eDMBNN93Eo48+yvTp07FarYwaNYpnn32W2bNnq22MHz+ew4cP88QTT2CxWLj11lu5//77T7v5GsCjjz7KXXfdxYwZM7j33nvZvn0748ePR6PRcPvtt/PAAw/w448/qvXvuece1q9fT8+ePSktLWXdunUMHjyYDRs2MGPGDMaMGUNJSQlRUVFce+21NUYoOnXq5LZS1NnmJ3z11Vfk5OTw2Wef8dlnn6nlMTExpKSkkJGRwSeffILD4WD69OlMn35yoYRJkyaxaNEiPD090Wq1pKamUlJSovb1+uuvdxv1ufLKyp9NVbudv//++zgcDqZNm8a0adNqtFtFURSmTp3KvHnz1IRvDw8PFi1axLRp07Barbz99ttEVVuQoqE0ysW+93ozkZaWRnR0NMeOHXOLeM8rSba+dEmytRCNojwtjT9nPI5XdEu6/eelGg95Bbt24rLZKU87RvaG9YT0G3Da/IgqLrudor17cNnslCYfJndjIpHX30DE0OvUOn/NfAZbQQG93nrH7VzF5WLDrTcTdcONtJ5yj1puycnBVlCAV3Q0Og8PSpOP8OeMf9Jz3hsYAwI4+P4CCnfvwiMikjb33o9XtPtIx6WuahTIbVQhs1qwUHrmPzuNQUGVgUJEBB4RkZV/hkfgER6OzsOjwe1YLBaSk5OJi4vDXC3Z/mwoTidFSXsrA9GAQPw6dLwg05kuBzabjZ07d9KlSxe3QKIuLpeLP//8k1atWhEQcDL3JDk5GafTWW+idmZmJhkZGbRt27bWFZ5OtWPHDqKiourN57jQTvd9LSMSQgghLkm2ggJ2vfA8ek9POj4xo9YHsYArKqcxBfXoQXDvPmx95EF0Hmairj99LoLWYCCga7fK83v1wr9LV3Y8NQOjnz9BJ6YIaI1GXKds2AWoZdpTHmTMISGYqz1EHPrgfSKHj8CzRQuSXn8Na24unZ96msx1P7P7xX/T++33LrkHTEVRsBcV1RhRsJx4fTY5LKag4BOBQuWIgmdEZOXIQli42wpbzYVGp3ObIieajlarxcvLi5KSEjWQUBSF4uLielc4qgoi2rRp06Agwmaz4XA4GiX5+kKSQEKckeJyH7LyIygs9cdi88Cgs+PrVURs+BE8zZUJWYoCWfnh5BaFUFrhg91pwGysINQ/m+jQVLRa1xld0+HQs3nfVdgdRjrG7iLEP6fOukczY0jJbIWnuZRe7Te7HTueG8mx7BgcTj2Bvnm0abEfve5kYpGiwJ8HehHsn01M2NFTmxZCXEQcZWXs+vdsHGVldHvxpdMu5wrgERGBd1w8Wb/80qBA4lR+7TtgDAgka8N6NZAwBgRSuHsXiqK4TZ2omrZSX7+yf/uV8rQ0Ov/rGRSnk5w/fqfLrOfwad0Gz+iWZKz9SU3WvthUBguFVBx3H1GoGmFwlpefWYMaDabg4MoRBXV0oXKEwRwWhs5kOj83Ii4LYWFhJCcn4+npiZeXF9nZ2bhcLnUVp+TkZAwGgzojJSMjg+PHjxMfH4/JZFJ3/9Zqteh0OpxOJ8ePHycgIACDwYDVaiUtLQ2TyXTaJPHmRgIJcUaOZcVQXOZHiH82Xh7HsNlNpOdGse1AL7q32YaXRxkul479xzri41lERFA6RoOd4jJfUjLjKCgNoGur7ZzJVMSUzDicrtNvwm61mUjNjkWrddQ4VlTqx8G0dkQFp2E2VXAsK4Yjx1vTNnq/WicjLxKHU090SGrDOyeEaHZcNhu7X/w35ceP02X2v2tNsq77XCsue82fIQ0+325zewj2josj838/UZ52zK0fxQcPAOBVLXm0OqfVypHFHxN7+0T0Xt7YCgtQHA6MAZW5FTqTCYO3t5pj0RwpiqLu2ly1ZKpFDRgycVoqzqxBrRZzcPDJEYXq05HCwmqM7gjRWAIDA3E4HBw/flzdkK5Nmzbq6MGpu2bn5OSgKEqNlaaqEro1Gg0VFRXk5eWpS8D6+voSFRVV68pRzZkEEuKMtAhNxcejBK32ZGpNSEAWW/f1JjU7hg4xe9FoXHRrsxU/r5Ob5kQEHcdktHA0M57C0gACfAoadL2yCi+O50YRE55CSmb9qz4cPt4aX88iFEWD3ek+NJhXHIy/dyGtWxwEQK91kJzRCk4EEg6HnpSMeNpE73e7NyHExUVxOtk791WK9++n01NP49e+fa11HBUVGE5ZO774wAFKjx4lbKD7jtPlaWloTSZ1ypHTYgGNpsan3DmJf+AoLcW71ck508G9+3D44w85/uMP6j4SiqKQseZHjEFB+LWr2T+AYyuWo/f2JuK6yp14DT6+aHQ6ytPT8IyKwl5cjK2o6Kz2C2hMiqJgy88/MZJw3H2EITMD1yk7Gp+WVos5JLTaiEIEHuGVeQvmsLDT7p8gxPkSGhpa51Smdu3aub3ucpqV37Ra7TntYN2cSCAhzkj14KCKp6kCL3MZ5ZbKFSy0WqXWesF+ORzNjKfc4tXgQOJQehuC/XPw8yqst15hqT85hSH0aLeFQ2k1/3G6XFr0upPzlPV6B07XyXnFKZlxeHmU1Ttt6rIgCfyXpssoef/woo/I27KZoF69cZSUkLXefUfdsMFDcFoq2HjP3YT2H4BndEt0ZjNlR1PI/DkBvacXLW8b73bOlgcfwK9TZ7q98CIAFRnH+WvWs4T2v7pyhSaNhpLDh8j+ZT3m0FBa3HByQydTcDBRN9xI2tcrUZzOyp2tN22kaO9e2j/6f7XmN1hycjj29Qo6PzNTPa7R6Qjq3YfDH32ANTeH3I0bMQUG4nvKA8z5oLhcWPPz1E3ZyjOOn9ycLSMD1ymfxp6WVotHWJj7iMKJBGdzaKgEC0JcRCSQEOdMUcDmMOJlrj8BzuaoHHY26GsmHtYmpzCE4jI/erXfhMVWdzKcosChtDZEBGXg7VF7H3w8i8lIa09+cSBmUwVp2dH4eFYGO2UWT47nRdK97dYG9UsI0XyVJicDkLdlM3lbNtc4HjZ4CFqjiYih11G4exc5iX/gstkwBgQSevVAYm4bhzk0rN5rmIKCCenbj8JdO8lc/zOKw4E5JJTI60cRM3YchlPmOMffMQmDtzfH16wh8+cEPCIiaf/IYzVGPqocWfQRgd17qIngVdpMvY8D775N8mef4hERSacn/9VoD92Ky4U1L6/WTdksWZlnHCxodDrMoWHqkqnVRxhMIaGX9H4YshimuJSc7vv50v2XLC6Y7IIwbHYzseHJ9dY7lhWDTusg0Of0c3qdLi2H09sQFXIMs8lSbyBxPDcKi81Ml9Y76qwTGpBFblEIu450A8BksNA5/i8ADqe3ITyw7iBECHHxqBo1qI/WYHBbcvV0Bq381u21wdeXtvdPq6N2TRqtlpa33kbLW29rUP2O/5xRa7nR35/O/3qmwdc9leJ0Ys3LrX1TtqxMdT+NhtLo9ZjDwtyXTK3aayEk5JJbTep0qubLl5eX43EGy8YK0ZyVn8j5qms1KQkkxDkpt3hyMK0dvp5FhAdm1FnvaFYMhaWBlSsl6U+fyHgsKwZF0Zx29SS7Q09KZjwx4SkY6xnp0GigU9xuKqweOJx6vMxlaLUucouCKSn3pUPMXqw2IwfS2lNa7oO3Zwlto/dhMpzhkL0QQjQhxenEkpPjluCsLp+alYXiOLNEco1eX2NEwRwegWdkJKag4MsuWKiPTqfD39+f7OxsADw9Pc96kzNx/lQlRlssFlyuM1tF8nKiKArl5eVkZ2fj7++Pro5/6xJIiLNmsxvZdaQrep2DjnG76lyJKbsglJSMeMIDjxMZnH7adi1WM8eyW9KmxQF01ZZnrU1yRjwGnZ2o4LQG9dnDdHKVEJdLw5H01sSEJWPQ29l+sDsmg5XO8TtJzYohKaUT3dpsb1C7QghxoShOJ5bsrJObsVVLcLZkn3mwoDUaMYeFu+crnBhhMAUFSbBwBsLDwwHUYEI0Pw6Hg9zcXEwmE/pLeIpdY/H391e/r2sj76A4Kw6njl1HuuJw6unWZludn9znlwSwL7Ujgb55bkut1iclMw6TwYqfdwEWa+WUpqr8CrvDiMVqxmS0UGHzICMvitZRB7HZT66e4lK0KIoGi9WMTufAUMcISFpONBqNQlRIOhabieIyf/p0+AOzyUJ85CE2J/XDajNhMlrP5K0RQlxGztcOxC6HA0t29sk8hcwMyo9Xbspmyc5Gcdb/IcuptEbjyYTm6huyRURgCgxCc5EtOdlcaTQaIiIiCA0NVfcOEM1LZmYm9913H+vXr6/3AVlUTmeqaySiigQS4oy5XFp2H+lKudWTLq2242WufeOg4jJf9iRfgY9nMR1jd6PRNCwBzWIzU2HzZHNSvxrHDqZVrlDSv/MGbDYToOFQelsOpddcqWlTUj+igo+pS75WZ7UbSc2KVftVFYgYDZVBg+nEn1a7BBJCiNrlJP7BoQ8XYss7mfdlDAqi9ZR7COlb8+fXqVx2e+XIwvETS6dWG2Gw5GTDGU670JrNJ6chnbIikjEgQIKFC0in0532AUw0Db1ez9GjR9Hr9Zib4a7mFxsJJMQZURTYm9KJ4jJfOsXvqnWZV6hcCWnXkS6YjRY6x+1EV89u1uUWT7RaJ+YTD+xxEUewO9yTesos3qRkxhMdehRfzyK0OideHmV0it1Zo73kzHicTj2tow5gNtW+4VHy8Vb4eRcS6Fu5u6xRXzmiUm71xNujjHJL5Xb2RsmREELUIifxD/a++nKNclteHntffZmOTzxJSN9+uGw2KrKy1JwFS/WchdzcMw4WdGYPPCLCK3dsPiXB2RgQIHPyhRAXlAQS4owcTm9DXnEIQb45OBx6svLdl0kMC8yqnPZ0uBsOp4Ho0FTyi4Pc6phNFW4ByJZ9V+HnVaDmI/h5F9W4rr6kcnqSj2cxwf65AGj1dvXv1aXlRAPUegyguMyH7MJQerY7uTSk2WTBx6OY/akdCQ88TmZ+JD6eRZiNZ7iZkhDikqc4nRz6cGG9dZJef41DH32ILS+38hOYM6Dz8KiWq1B9RaQIDH7+EiwIIZoNCSTEGSmtqNywLK84hLzikBrHwwKzcDgMWO2Vw4XJGa1r1gnIqHMk43xTFDiU3pao4HQ8ze6jFR1i97A/tT3JGa3w9iihXcukJumjEKL5clSUk5mQ4DadqTaK3Y4tt+4NLnWeXnhGRpzYlK1agnNEJAZfXwkWhBAXBQkkxBlpyCpGZpOFQd1+bnCbDanr71PY4Dbr66NGA93bbqv1mIepQlZpEkKobIWFlCYfofTIEfXPisyMBo8waE0mvFrGnJKzUBks6H18JFgQQlz0JJAQQghxWVMUBUt2llvAUJp8BFt+/jm1e8UzM/HvfEUj9VIIIZofCSSEEEJcNhSnk7K0Y6cEDck4y0+/s73WaMSrZQxecXHk/vE7jrK6zzEFB+PXoWNjdl0IIZodCSSEEEJckpxWK2Upye7Tk44eRWnA+v56Ly+84+Lxjo+v/DMuHs8WLdQ9IgKv7F7rqk1VWt39D9nITQhxyZNAQjR7igJFpf7YHCaMeit+3oV17qIthLg82YuLKwOF5JOBQ/nx9AYtr2oMClKDBZ/4yuDBFBJabw5DSN9+dHziyRr7SJiCg2l19z8atI+EEEJc7CSQEM1aTmEIh9LbYLOf3DTGaLDQOuogIf51r4gihLg0KYqCNTeX0iOH3UYarLm1L/fsRqPBIyLy5CjDiT+Nfn5n1ZeQvv0I7t3nvOxsLYQQFwMJJESzlVMYwt6UzjXKbXYTe1M60zF2twQTQlzCFKeT8uPHa6yc5CgtOe25Gr0er5Yx7kFDTCw6D49G7aNGp5OEaiHEZUsCCdEsVe730ObEq1OnF2gAhcPpbQj2y5FpThe54nIfsvIjKCz1x2LzwKCz4+tVRGz4kRp7fZRZPDmc3oaiMj+0GoVA3zxaRR3EqD/9nPfqKqwebNnXG0XR0b3tFnw8Tz6YFpb6k5YdTWmFDzaHAb3OgbdHKTFhKTU2SzyeG8mx7BgcTj2Bvnm0abEfvc6pHlcU+PNAL4L9s4kJO3oW787lw2WzUXr0KKXJh9UE6LKjKbis1tOeq/PwwDsuDu+4Vmrg4NmiBVqD4QL0XAghLl8SSIhmqajU3206U00arHYziXv642Uuw8NUgdlY4fZn9Qc60Xwdy4qhuMyPEP9svDyOYbObSM+NYtuBXnRvsw0vj8qVcaw2E38d7I5O5yAu4ghOl4607JaUVXjRve1WtNqG7x58OL01Go1S63YAFdbKT6wjgtIxGmw4nHqy8sPZcag7V8T/RaBv5ZKgRaV+HExrR1RwGmZTBceyYjhyvDVto/erbWXkReJw6okOST2Hd+jS4ygrrcxlqDbKUJZ2rEH5DAZ/f3WEwefEn+awcDRa7QXouRBCiOokkBDNks1halA9u8NEYamJwtKax/Q6Ox7GCsxuQYYFD1MFJoNFRjKaiRahqfh4lLgFAiEBWWzd15vU7Bg6xOwFIDUrBqdLR/d2WzAbKz+l9vUsZufhK8nMjyAy+HiDrpdfHEh+SRDRoUdJzYqrcTwiKIOIoAy3ssjgdDbt7UtaTrQaSOQVB+PvXUjrFgcB0GsdJGe0ghOBhMOhJyUjnjbR+88oyLmUKIqCLT//RBL0yelJlqysBp1vDg+vlsvQCp/4eIwBAee510IIIRqqSQOJDRs2MGfOHLZt20ZGRgYrV67k5ptvVo8risKsWbNYuHAhhYWF9O/fn/fee482bdqodfLz83nwwQf57rvv0Gq13Hrrrbzxxht4e3urdXbu3Mm0adPYsmULISEhPPjggzzxxBNuffnyyy959tlnSUlJoU2bNrzyyitcf/315/09ELUz6k8/nQFAq3HiUmpPbHQ4DZRUGCip8K1xTKNxYTZYMJsqqgUbFnVEQ0YzLhw/r+IaZZ6mCrzMZZRbPNWynKJQgvxy1SACIMCnAA9TGTmFoQ0KJFyKhkPpbYgKOYaHseK09avotC6MejsO58kfmS6XFr3u5JQqvd6B03XyezElMw4vj7LLJo9HcbmoyMiokc9gLy467bkanQ7P6Gh15STv+Hi8Y+PQe3ldgJ4LIYQ4W00aSJSVldG1a1fuvvtuxowZU+P4q6++yptvvsnixYuJi4vj2WefZfjw4ezduxezuXLay8SJE8nIyGDt2rXY7XYmT57M1KlT+fzzzwEoLi5m2LBhDB06lAULFrBr1y7uvvtu/P39mTp1KgB//PEHt99+Oy+99BI33HADn3/+OTfffDN//vknnTvXTPYV55+fdyFGgwWb3UTNHAkABZPBSu8Of+B06amwemCxeVBh88BiPfmntY7zFUVLhc2TCpsnBbW0btDZqgUZlsoRjRMBh8lgldGM80xRwOYw4mWumtZkxO4w4u1RM8nWx7OE/OKgBrWbnhONw2kgJiyF3MKQeus6nDoURYvdYSAzP5wyizctw1KqXbeYjLT25BcHYjZVkJYdjY9nZVBUZvHkeF4k3dtubeAdX1xcdjtlx1LdpyalpOC0nD4405pMeMfGnQwY4uPxim6J1mi8AD0XQgjRmJo0kBg5ciQjR46s9ZiiKMyfP59nnnmG0aNHA/DJJ58QFhbG119/zYQJE0hKSmL16tVs2bKFnj17AvDWW29x/fXXM3fuXCIjI1myZAk2m42PPvoIo9FIp06d2LFjB/PmzVMDiTfeeIMRI0bwz3/+E4B///vfrF27lrfffpsFCxZcgHdCnEqjgdZRB0+s2qTgHgxUThNpFXUQrRa0WgcGfQm+XjUfMl0uDRabuZYgw0yFzQOXq/Z/AnanEXu5kZLymstCajSuaoGFe5DhYbSgk9GMc5ZdEIbNbiY2PBk4OdXNZLDVqGs0WHE4DbhcmnqnENnsRo5mxhIfeahBI057UzpTUFIZoGg0LiKC0ompFkiEBmSRWxTCriPdTvTNQuf4vwA4nN6G8MAMvD1Ov1tyc+eoKHfLZyhLPkLZsWMoDsdpzzX4+tbY1M0jIkKWRxVCiEtEs82RSE5OJjMzk6FDh6plfn5+9OnTh8TERCZMmEBiYiL+/v5qEAEwdOhQtFotmzZt4pZbbiExMZGBAwdirPZp1/Dhw3nllVcoKCggICCAxMREHnvsMbfrDx8+nK+//rrO/lmtVqzVVhMpKTn9coTizIT459AxdneNfSRMBiutGriPhFar4GmuqLH6D1R+6m13GtxGMCw2szq6Ue9ohtWLCqsX1PK/3aC3uSd+nwg4PIzlGA02Gc04jXKLJwfT2uHrWUR4YGWugtNVmUir0dRMxtWeKHO5dGi1dT/cHjneCrOxgoighuVSxEccxhaaitVmJis/HEXRoCgn/+dpNNApbjcVVg8cTj1e5jK0Whe5RcGUlPvSIWYvVpuRA2ntKS33wduzhLbR+2oNhpoLW2GB2yhDafIRKjIyTn8iYAoJdVtq1ScuHmNQUL2bugkhhLi4NdtAIjMzE4CwsDC38rCwMPVYZmYmoaGhbsf1ej2BgYFudeLi4mq0UXUsICCAzMzMeq9Tm5deeonnnnvuLO7s8rGn0MyqY35sy/XkeIUBP4OTKwIquL99DjHetS/X6XDB7b/EkVxq4uGO2dzRKodgvxyKSv35ITWYb9I9OWK1oRwJAtyns3zQ/yjdAisDht0FldfeXejBwWITTkXD1hv3udXXaMCot2PU2/GtZZ6+y6WtDCyqjWBYqk2hcrlq/1TV7qichlP7aIYTD2O13Aw1yKgc0dBpT79qzaXMZjey60hX9DoHHeN2qUFX1fuiKDVX5nGdKNNq6x5lKC7zJasgnC6ttjc4kPP2PJnBHxaQybYDvdiX2oFOcbvd6nmYTgapLpeGI+mtiQlLxqC3s/1gd0wGK53jd5KaFUNSSie6tdnesA6cR4qiYMnKqpHPYCvIP/3JWi2eUVE1RhoMPj7nv+NCCCGalWYbSDR3Tz31lNsoRnp6Oh07dmzCHjU/iw8F8le+J0Mji2njYyXXqufLlAD+viGOjwek0Nq35iezS5MDyKxwX/tdowF/n0J6RVfg71dzNad3kkKocGrp5H/yge73bG++TvWnja+FKE8bqWUNWwWqOq3Whae5HE9zeY1jigJ2h6FGTkaFrTLQqMztqElRdJRbvSi31p5EatRbK4OMN17HHBaGOSwcj7BwzOHhGAMCLulPdx1OHbuOdMXh1NOtzTa3T+6rku+t9prz6G12E3qdvd5pTUeOt8bPqxAPowWLtXJ0y+40qG0abCa3JO5TabUKQb65HMuOwenS1hnwpeVEo9EoRIWkY7GZKC7zp0+HPzCbLMRHHmJzUj+sNhOmeq7V2FyKhvIU96VWS1OScZbX/L4+ldZoxCsm1i1o8IqJQWc6839PQgghLj3NNpAIDw8HICsri4iICLU8KyuLbt26qXWys7PdznM4HOTn56vnh4eHk3XKUoNVr09Xp+p4bUwmE6Zqv0yLi2t+on25mxhfwH+6H8dQ7UPkYZHFTPgljsWHgvh3d/cpE/lWHR8cCGZS6zwW7K+ZCNvG10obX/cHsMwKPdkWPTe3LHK7ztjYAia1zsOsU3hlV9hZBRL10WjAaLBjNNhrXXXIeWI0o2aQUVlW10pTNocJm8NE8fp1NY5pjUbMYWF4hEfUCDLMoaEX9cOdy6Vl95GulFs96dJqO16nBG8mow2D3kZpRc1PvUvKfWpNwq7OYjNhtXuwKalfjWN7krui09oZ0OXX+vuoaAENTqeu1kDCajeSmhVLx9jdaDSKGkwaDZXfsyZDVTB0/gIJp1NLqcWb0gofSst9KK3wpszihfLow6c9V+/l5T7KEN8Kz6goyWcQQghRp2YbSMTFxREeHk5CQoIaOBQXF7Np0ybuv/9+APr27UthYSHbtm2jR48eAPz888+4XC769Omj1nn66aex2+0YTuxyunbtWtq1a0fAifXI+/btS0JCAo888oh6/bVr19K3b98LdLeXpq6BNfMSWnrbifexkVxa86H3raQQYrxtjGxRXGsgUZs16b4oaBgR5b7EZJCpaROedVoXXubyGg/EcHJForqCjLr20HDZbJQfO0b5sWO1HjcGBlYGF+HhJ4KMMMzhla8Nfv7NdjRDUWBvSieKy3zpFL+r1sAMINgvm6z8CCzVRg8KSgKosHrRIuTke+JSNFisHuh0DnVUo230flwu92lRhaUBpOdGEx95EE/Tyf9PNrsBo8F96p3DoSe3MBSTwVLjWJXk463w8y5U95kw6iuvXW71xNujjHJL5SiUsZFyJOwOA6UVVUFD5Z/lVk9qX+XMnTEoSJ2S5HNi5SRTSGiz/R4RQgjRPDVpIFFaWsqhQ4fU18nJyezYsYPAwEBatmzJI488wgsvvECbNm3U5V8jIyPVvSY6dOjAiBEjuOeee1iwYAF2u53p06czYcIEIiMjAfjb3/7Gc889x5QpU5gxYwa7d+/mjTfe4PXXX1ev+/DDDzNo0CBee+01Ro0axdKlS9m6dSvvv//+BX0/LgeKUjnyEO/j/ons7gIz3x/z44P+RxvwGHTS6nRfwsx2ugc1fE+ApqbRVK4+ZDLY8KPmGvtOpxbLlF1UZGViycrEkpVFRWYmlsxMKrKzUOy1P8ja8vOx5edTnLS3xjGtyVQ5ehEWhjk8Ao/wsJNBR2gYWoOhlhYvjMPpbcgrDiHINweHQ09W/in5SoGVo4Utw46SUxjKX4eupEVIGk6XjmPZLfEyl6pJ2QA2m4kt+64iLCCD9jFJAOrDfXVVe0L4exfi43lyRGPXka6YDFZ8vIox6m1YbGay8iOw2k10jN1dox2A4jIfsgtD6dlus1pmNlnw8Shmf2pHwgOPk5kfiY9nEWaj5YzeH0UBq92sBgtVwYO13p3f1bPxMJXj3WuEWz6D0a9m/o4QQghxppo0kNi6dStDhgxRX1flHEyaNIlFixbxxBNPUFZWxtSpUyksLGTAgAGsXr1a3UMCYMmSJUyfPp1rr71W3ZDuzTffVI/7+fnx008/MW3aNHr06EFwcDAzZ85Ul34F6NevH59//jnPPPMM//rXv2jTpg1ff/217CFxHvyY7ku2xcC97XLVMkWBObvDuC6qmC6BFo6XN+yh9nCJkYPFZu5slXdJrYSk07nwionBKyamxjHF5cJWUEBFZsbJACMrU/3TXlT75l8uq5Wy1KOUpR6teVCjwRQYVDltKrM9ZqP7Rn0Gnf28vr+lFZWbR+YVh5BXXHMkqiqQOFyuYZuSSkBZJMWW1rhQyKOUq6KT3PIj9hdXjuj8muXNpF3tak20r+6/RwL4MTuE7Ao9ASYnNweX0QVPirOjcTj16PUOfD2LKPZM5pG/vEguDcBT72JgWCkPdcjBz+jkUHpbooLTa6wO1iF2D/tT25Oc0QpvjxLatUyq971QlMoRjMppSSeDBofz9P8mNBoXXuZSvD1K8fYowduzBG9zWeVyxP+XcNrzhRBCiDOlURSl7gxF0WBpaWlER0dz7NgxWrRocWEu+pz36es0IyklRib9FkMrHysL+6eiO/Fw+m2qH6/sCmP5NUcI93BwvNzATQmtTqzaVPcqMu8kBfPxoWD+Oyi5Ru5Eda/sCuPLlIB6HyabnVmlp69TC2dFxYmRjFODjCws2VkNWvv/VDqto8bqUupu4AZLvUnOjemJrZG1Ju+XO7Ruyfv/b38wHx8Moo2vhTKHltQyU63/710K3PVbDMklJsbGFhDjZeNYuZGvUvzx0rv4ckgyXvrKXIivUvx5eVc4vYPLGBJRQnaFnv8mBxLtZWPRgKOYdGf+HjhdWsoqvNwChrIK7zrzZ6rTaR0nAwaPErw9S/E0ldX9/+Isv5+EEOJS0yTPa5ewZpsjIS4tuRYdD29ugbfexSs9j6tBRKldyzv7QrizdT7hHg1/yFUUWJ3uRysfS71BxOVG5+FRuWtwbFyNY4rTibUgv3KKVGZVsFE5smHJysRex4IBTpeeMosPZZbalves3GG8enBRtayth6kCvc7RaKMZDU3eb2ii/a4CD/YWevBE50zGxRWq5TFeNp7/K4LNOZ4MiSjF7oJ39oXQPbCcd646pt5Pl8AKHt0czcpUfybE1bY/+kl2h/5kPsOJwKHc4gnUXM72VEa9VQ0WKgOHUszGiktqFE4IIcTFSQIJcd6V2rU8tCmaUruOhf2PEmI+GTB8djgQu0vDdZHF6pSm7IrKb8tim5bj5QZCzHa3h0eAv/I9yKgwML19/Q9w4iSNToc5OARzcAj+na+ocdxRXo7luXi3xO/qG/UptT70arDazVjtZooIqHFUp3WoG/OdDDIseJgqMBktaDUN/yS/ocn7DU20L3NU3k/gKfWDT3x/Vo0yHC42UWLXcV1UsdvD+9VhZXjqnPyU7qMGEopSuRdG9YChtMIHi82jQX0yG8srRxo8K0cafDxKGy05WwghhGhsEkiI88rq1PDo5haklhl596pU4n3cH4oyKwwU23WMWx9f49yPD1VOXVoyMJl2fu6jDj+m+6JBYUSULLvbWPSenpWfenvWnAajKBqsNpMaZFTt/l0VbNQ1h9/p0qsP1TUplfkY6i7gFrcdwQ36049Q1ZW83xAd/Sx46Fws2B+Mn9FZObWpzMCbe0Po6F9B7+AyAGyuyujBdMq0IUWBCKMOky2Aw+kGyiyVgYPdUXOvi1NpcOFpLqsWNFSONuh1TbvamBBCCHEmJJAQ541Tgae2RbKzwIPXeqXRJbDmajUT4vIZHO6+B0C+TceLOyO4MbqQQWGlRHmeshSnC/533IdugRWEe575nH9x5jQaBbPJgtlU+4pDDoeeCpu5ctfvqt2/q4INm4nap/BoThz3oLCWKfx6nb1yJOPECEb1IMNstKLRKLUm75+OokBRqT82h4kXOxfz4j4v7k9sqR7vG1LKKz3T0Z/ocksvO3oUDucHkOHlqY40lJT78EBwZT5DWk5QbZcCQKvmM5ycmuRlLr1guSVCCCHE+SKBhDhv5u8JZUOWD1eHlVBs1/FDmq/b8etbFNPe30p7f/dPk6umOMX72BgcUfMJMzHHiyK7nhFRdT88ZpTr+T6tconLpMLKVb4+OFD5sBfhYWdUtIxkNCa93oGPvhSfOkYzLDZTLUFG5ciG01X7aIbDaaC0wkBphW8tR13o9VYyyzRMDbfSzeRHTqGxWm5G7Z/s5xSGcCi9DbZqS6c+FGKn2JxOWEA2B4pMfHEkmDd3xPP3GKu6R8NLLbzRouFA7Vt4qAw6W7VchsqgwcNULvkMQgghLkkSSIjzZn9x5cPar1k+/JpVc2rL9S3O7mH+xzQ/9BqFoZF1n59ebqyxqV3V6+5B5RJIXEAajYKHyYKHyUKAT82cFrtDX2uQYTkxjar2Dda0OBwetDIBmEnJcN8XQa+z42GsoI1LYaSfloy8CKw2E0ezaiahe2j0eFpj8CkOoKvTQLsITwAOp1e7Wi1dqFBsFClWdpQoPHDFUUK9izEarBI0CCGEuGxIICHOm/f7pZ7VeZGe9nqXan2xx/HTttEzuPziWu71MmbQOzDoS9w2haviUjRY1ZwMMxU2D0orPDlU6IuPxoBJW/uqRw6ngZIKA0HAUF84cKxDndfXnAhUSsrr2qRNUfMZFH0ZpViI9C2ihY+Fu39ryTGLkejAnDO9bSGEEOKiJ4GEEKLZ0moUPEyV05WgMnl/+sZokoqsvNMnlY5+jpMjGdWSvy1WD6x2E7WPZtRNgxOzuYyEXB0t/Qu5Me44Xh5l6LSuGnVL7FqSisxcE1EzABJCCCEuB6dfxFwIIZqB6sn7L/dIp2uQBYPega9XCaEB2cSEHaVdy310a72dqzr9wdVd1rNDe5D3c/IICzz9KBZAu5b7OGw8zPKCIkICjuPrVVJrEAHwdlIITpeGv8XLEsRCCHGpe+edd4iNjcVsNtOnTx82b95cZ92FCxdy9dVXExAQQEBAAEOHDq1RX1EUZs6cSUREBB4eHgwdOpSDBw+e79todDIiIYS4KDQkeR/cE+23F2nZb7Gyraychuxf+uUxTz495k+8j5Uh1UYaFh0M5HCJiU4BFvQahfWZ3mzM8eb+djl08q99JSshhBCXhi+++ILHHnuMBQsW0KdPH+bPn8/w4cPZv38/oaGhNeqvX7+e22+/nX79+mE2m3nllVcYNmwYe/bsISoqCoBXX32VN998k8WLFxMXF8ezzz7L8OHD2bt3L2azuUabzZVGURRZg7ARNMmW6895X5jriAtvVi3roV4Izfh7auofLfkzz7PO41U5MVtzPbmv2nKuUDnB6ZmIMPx0WjS1ZEMrikKR08mC/HQGhJUxrX0O/tU2qvsty4uFB4JJKTXiVDS08bUyMT6foZEXybSmpvp+EkKIZuZsntf69OlDr169ePvttwFwuVxER0fz4IMP8uSTT572fKfTSUBAAG+//TZ33nkniqIQGRnJ//3f//H4448DUFRURFhYGIsWLWLChAlnf4MXmIxICCEuCg1N3q8r0T6nMI+9KZ0BBffcCQWNBvq1TmK0f+1J0wPCyhgQVnbmnRZCXJYcDgfr1q1j586dWCwWwsLCGDJkCK1atar3vNzcXLZu3Up6ejoZGRk4nU4efvhh/P39a71GYmIiO3fupLCwEA8PD6Kjoxk0aFCtn5ILdyUlJRQXn1zB0WQyYTKZatSz2Wxs27aNp556Si3TarUMHTqUxMTEBl2rvLwcu91OYGAgAMnJyWRmZjJ06FC1jp+fH3369CExMfGiCiQkR0IIcVkI8c+hY+xujAb3fUtMBisdY3cTUkcQIYQQZ+qbb75h48aNXHHFFYwYMQKNRsPnn39Oamr9H4ikpaWxefNmbDYbISEh9dZdsWIF69evJzY2lpEjR9KjRw+OHj3Khx9+SGFhYSPezaWpY8eO+Pn5qV8vvfRSrfVyc3NxOp2EhYW5lYeFhZGZmdmga82YMYPIyEg1cKg671zabC5kREIIcdkI8c8h2C9H3dnaqLfi510oez8IIRpNeno6u3fv5rrrrqNfv34AdO3alXfffZe1a9cyZcqUOs9t164dM2bMwGQy8ccff9T5UFlcXExSUhJ9+/Zl2LBhannLli355JNP1GOibnv37lXzFYBaRyMaw8svv8zSpUtZv379RZX70FAyIiGEuKxoNODvU0hoQBb+PhJECCEa1969e9FoNPTo0UMt0+v1XHnllaSlpVFUVFTnuR4eHg16oLXZbAB4e7vntfn4VG7+ajAYzqbrlxUfHx98fX3Vr7re9+DgYHQ6HVlZWW7lWVlZhIeH13uNuXPn8vLLL/PTTz/RpUsXtbzqvLNps7mRQEIIIYQQopFkZmYSFBRU48G06tPvxpi6EhAQgK+vL4mJiezfv5/i4mLS09NZtWoV/v7+dO7c+ZyvISoZjUZ69OhBQkKCWuZyuUhISKh31OfVV1/l3//+N6tXr6Znz55ux+Li4ggPD3drs7i4mE2bNl10I0kytUkIIYQQopGUlJSoIwPVVZWVlJz7am86nY5x48axfPlyli5dqpZHREQwZcqUS3IKTVN67LHHmDRpEj179qR3797Mnz+fsrIyJk+eDMCdd95JVFSUmmfxyiuvMHPmTD7//HNiY2PV4NHb2xtvb280Gg2PPPIIL7zwAm3atFGXf42MjOTmm29uqts8KxJICCGEEEI0EofDgU6nq1Gu1+vV443BbDYTHh5Ox44dadGiBfn5+fz22298+eWX3HHHHer1xLkbP348OTk5zJw5k8zMTLp168bq1avVZOnU1FS02pOTfN577z1sNhtjx451a2fWrFnMnj0bgCeeeIKysjKmTp1KYWEhAwYMYPXq1RddECjfZUIIIYQQjUSv1+N0OmuUVwUQjfGAb7FY+Pjjj+nXr5+a0A0QGRnJ4sWL2b59O7169Trn64iTpk+fzvTp02s9tn79erfXKSkpp21Po9Hw/PPP8/zzzzdC75qO5EgIIYQQQjQSHx+fWqcvVZXVNu3pTCUlJVFWVka7du3cymNjYzGZTBw7duycryFEQ0ggIYQQQgjRSMLCwsjLy8Nqdd+zJj09HaBRVuUpLa3crV5RFLdyRVFwuVy4XK5zvoYQDSGBhBBCCCFEI+nYsSOKorBt2za1zOFwsGPHDqKiovDz8wOgqKiI3Nzcs7pGUFAQALt373Yr379/P3a7/aJbQlRcvCRHQgghhBCikbRo0YKOHTuSkJBAWVkZgYGB/PXXXxQWFnLTTTep9VauXMnRo0eZNWuWWmaxWNi8eTOAOj1p8+bNmM1mzGYzvXv3Bio3rgsJCeGXX36hqKiIqKgo8vPz2bJlC97e3nTv3v0C3rG4nEkgIYQQQgjRiG655RZ+/vlndu7cSUVFBWFhYdx+++3ExMTUe57FYmHdunVuZYmJiQD4+fmpgYROp2Py5Mls2LCBgwcPsmvXLkwmE+3bt+eaa67B09Pz/NyYEKfQKKdOsBNnJS0tjejoaI4dO0aLFi0uzEWf8z59HXFxmlXaNNeV76lLU1N9PwkhRDPTJM9rlzDJkRBCCCGEEEKcMQkkhBBCCCGEEGdMciSEEEIIIZoJl8tFamoqJSUl+Pj40LJlS7ddk4VoTiSQEEIIIYRoBpKSkli9ejXFxcVqma+vLyNGjKBDhw5N2DMhaichrhBCCCFEE0tKSmLZsmVuQQRAcXExy5YtIykpqYl6JkTdJJAQQgghhGhCLpeL1atX11tn9erVsmO1aHYkkBBCCCGEaEKpqak1RiJOVVxcTGpq6gXqkRANI4GEEEIIIUQTyszMbFC9kpKS89wTIc6MBBJCCCGEEE1k9+7d/Pzzzw2q6+Pjc557I8SZkVWbhBBCCCEuMLvdzo8//sj27dsbVN/X15eWLVue514JcWYkkBBCCCGEuICysrL46quvyM3NVctatmxZbw7EiBEjZD8J0exIICGEEEIIcQEoisK2bdtYs2YNDocDAIPBwPXXX0/Xrl3Zt2+f7CMhLioSSAghhBBCnGcVFRV89913bvtBhIWFMXbsWIKDgwHo0KED7dq1k52txUVDAgkhhBBCiPPo2LFjLF++nKKiIrWsV69eDBs2DL3e/VFMq9USGxt7gXsoxNmRQEIIIYQQ4jxwuVz8/vvvrFu3DkVRADCbzYwePZr27ds3ce+EOHcSSAghhBBCNLKSkhJWrlxJcnKyWtayZUvGjBmDn59fE/ZMiMYjgcRFLLbii6bugjhPUpq6A0IIIc7awYMH+frrrykvL1fLBg4cyKBBgyTfQVxSJJAQQqgkOL00pTR1B4S4TDidThISEkhMTFTLfHx8GDNmjOQ9iEuSBBJCCCGEEOcoPz+f5cuXc/z4cbWsbdu2jB49Gk9PzybsmRDnjwQSQgghhBDnYNeuXaxatQqbzQaATqdj6NCh9OnTB41G08S9E+L8kUBCCCGEEOIs2Gw2fvzxR3bs2KGWBQYGMnbsWCIiIpquY0JcIBJICCGEEEKcoczMTJYvX05ubq5a1qVLF66//npMJlMT9kyIC0cCCSGEEEKIBlIUhS1btvDTTz/hdDoBMBgMjBo1iq5duzZx74S4sCSQEEIIIYRogIqKCr799lv27dunloWHhzN27FiCgoKasGdCNA0JJIQQQgghTiM1NZXly5dTXFyslvXp04ehQ4ei18vjlLg8yXe+EEIIIUQdXC4Xv/76K7/88guKogDg4eHB6NGjadeuXRP3ToimJYGEEEIIIUQtiouLWblyJSkpKWpZTEwMY8aMwdfXt+k6JkQzIYGEEEIIIcQpDhw4wNdff01FRQUAGo2GQYMGcfXVV6PVapu4d0I0DxJICCGEEEKc4HA4+N///semTZvUMl9fX8aMGUNMTEwT9kyI5kcCCSGEEEIIIC8vj+XLl5ORkaGWtWvXjptuuglPT88m7JkQzVOzHptzOp08++yzxMXF4eHhQatWrfj3v/+tJjtB5XrOM2fOJCIiAg8PD4YOHcrBgwfd2snPz2fixIn4+vri7+/PlClTKC0tdauzc+dOrr76asxmM9HR0bz66qsX5B6FEEII0fR27tzJ+++/rwYROp2OkSNHMn78eAkihKhDsx6ReOWVV3jvvfdYvHgxnTp1YuvWrUyePBk/Pz8eeughAF599VXefPNNFi9eTFxcHM8++yzDhw9n7969mM1mACZOnEhGRgZr167FbrczefJkpk6dyueffw5UJlMNGzaMoUOHsmDBAnbt2sXdd9+Nv78/U6dObbL7F0IIIcT5ZbPZ+OGHH/jrr7/UsqCgIMaOHUt4eHgT9kyI5q9Zj0j88ccfjB49mlGjRhEbG8vYsWMZNmwYmzdvBipHI+bPn88zzzzD6NGj6dKlC5988gnHjx/n66+/BiApKYnVq1fzwQcf0KdPHwYMGMBbb73F0qVLOX78OABLlizBZrPx0Ucf0alTJyZMmMBDDz3EvHnzmurWhRBCCHGeZWRk8P7777sFEd26dWPq1KkSRAg377zzDrGxsZjNZvr06aM+i9Zmz5493HrrrcTGxqLRaJg/f36NOrNnz0aj0bh9tW/f/jzewfnRrAOJfv36kZCQwIEDBwD466+/+O233xg5ciQAycnJZGZmMnToUPUcPz8/+vTpQ2JiIgCJiYn4+/vTs2dPtc7QoUPRarVqIlViYiIDBw7EaDSqdYYPH87+/fspKCiotW9Wq5Xi4mL1q6SkpHFvXgghhBDnhaIobNq0iQ8//JC8vDwAjEYjt9xyC6NHj3Z7HhDiiy++4LHHHmPWrFn8+eefdO3aleHDh5OdnV1r/fLycuLj43n55ZfrDUg7depERkaG+vXbb7+dr1s4b5r11KYnn3yS4uJi2rdvj06nw+l08p///IeJEycCkJmZCUBYWJjbeWFhYeqxzMxMQkND3Y7r9XoCAwPd6sTFxdVoo+pYQEBAjb699NJLPPfcc41wl0IIIYS4UMrLy/nmm2/UDykBIiIiGDt2LIGBgU3YM9FczZs3j3vuuYfJkycDsGDBAr7//ns++ugjnnzyyRr1e/XqRa9evQBqPV5Fr9df9CNfzXpEYtmyZSxZsoTPP/+cP//8k8WLFzN37lwWL17c1F3jqaeeoqioSP3au3dvU3dJCCGEEPVISUlhwYIFbkHEVVddxZQpUySIuMyUlJS4zSyxWq211rPZbGzbts1t9otWq2Xo0KHq7JezdfDgQSIjI4mPj2fixImkpqaeU3tNoVmPSPzzn//kySefZMKECQBcccUVHD16lJdeeolJkyapUVxWVhYRERHqeVlZWXTr1g2A8PDwGkNPDoeD/Px89fzw8HCysrLc6lS9ritSNJlMmEwm9XVxcfE53KkQQgghzheXy8WGDRvYsGGDuvKjp6cnN998M23atGni3omm0LFjR7fXs2bNYvbs2TXq5ebm4nQ6a539sm/fvrO+fp8+fVi0aBHt2rUjIyOD5557jquvvprdu3fj4+Nz1u1eaM06kCgvL6+xe6ROp8PlcgEQFxdHeHg4CQkJauBQXFzMpk2buP/++wHo27cvhYWFbNu2jR49egDw888/43K56NOnj1rn6aefxm63YzAYAFi7di3t2rWrdVqTEEIIIS4OxcXFrFixgqNHj6plsbGxjBkz5qJ6YBONa+/evURFRamvq384fCFU5fsCdOnShT59+hATE8OyZcuYMmXKBe3LuWjWgcSNN97If/7zH1q2bEmnTp3Yvn078+bN4+677wYqt6t/5JFHeOGFF2jTpo26/GtkZCQ333wzAB06dGDEiBHcc889LFiwALvdzvTp05kwYQKRkZEA/O1vf+O5555jypQpzJgxg927d/PGG2/w+uuvN9WtCyGEEOIc7d+/n2+++YaKigqg8rlh8ODBDBgwoMYHleLy4uPjg6+v72nrBQcHo9Ppap250pj5Df7+/rRt25ZDhw41WpsXQrMOJN566y2effZZHnjgAbKzs4mMjOTee+9l5syZap0nnniCsrIypk6dSmFhIQMGDGD16tXqHhJQubzr9OnTufbaa9Fqtdx66628+eab6nE/Pz9++uknpk2bRo8ePQgODmbmzJmyh4QQQghxEXI4HKxdu9ZtiU5fX19uvfVWWrZs2YQ9Excbo9FIjx49SEhIUD+kdrlcJCQkMH369Ea7TmlpKYcPH+aOO+5otDYvhGYdSPj4+DB//vxa19+totFoeP7553n++efrrBMYGKhuPleXLl268Ouvv55tV4UQQgjRDOTm5rJ8+XJ1ZUaA9u3bc9NNN+Hh4dGEPRMXq8cee4xJkybRs2dPevfuzfz58ykrK1NXcbrzzjuJioripZdeAioTtKsW4bHZbKSnp7Njxw68vb1p3bo1AI8//jg33ngjMTExHD9+nFmzZqHT6bj99tub5ibPUrMOJIQQQgghGkJRFP766y9++OEH7HY7UJlXOXz4cHr27IlGo2niHoqL1fjx48nJyWHmzJlkZmbSrVs3Vq9erSZgp6amuk2VO378OFdeeaX6eu7cucydO5dBgwaxfv16ANLS0rj99tvJy8sjJCSEAQMGsHHjRkJCQi7ovZ0rCSSEEEIIcVGzWq388MMP7Ny5Uy0LDg5m7NixNVbbEeJsTJ8+vc6pTFXBQZXY2Fh1dbC6LF26tLG61qQkkBBCCCHERev48eMsX76c/Px8tezKK69kxIgRskO1EOeZBBJCCCGEuOgoisLGjRv53//+py4LbzQaufHGG+ncuXMT906Iy4MEEkIIIYS4qJSVlfHNN99w8OBBtSwyMpKxY8fK/k9CXEASSAghhBDiopGcnMyKFSsoLS1Vy/r168c111yDTqdrwp4JcfmRQEIIIYQQzZ7L5eKXX35hw4YNapmnpye33HKLuqSmEOLCkkBCCCGEEM1aUVERK1asIDU1VS2Li4vjlltuwcfHpwl7JsTlTQIJIYQQQjRbSUlJfPvtt1gsFqByI9ohQ4YwYMAA2RtCiCYmgYQQQgghmh2Hw8GaNWvYunWrWubn58ett95KdHR0E/ZMCFFFAgkhhBBCNCs5OTksX76crKwstaxjx47ceOONmM3mJuyZEKI6CSSEEEII0SwoisKOHTv48ccfsdvtAOj1ekaMGEH37t1lKpMQzYwEEkIIIYRoclarlVWrVrF79261LCQkhLFjxxIaGtqEPRNC1EUCCSGEEEI0qfT0dJYvX05BQYFa1r17d0aMGIHBYGjCngkh6iOBhBBCCCGahKIoJCYmkpCQgMvlAsBkMnHjjTfSqVOnJu6dEOJ0JJAQQgghxAVXVlbG119/zaFDh9SyqKgobr31VgICApqwZ0KIhpJAQgghhBAX1JEjR1i5ciWlpaVqWf/+/RkyZAg6na4JeyaEOBMSSAghhBDignA6naxfv57ffvtNLfPy8uKWW26hVatWTdgzIcTZkEBCCCGEEOddYWEhy5cvJy0tTS1r1aoVN998M97e3k3YMyHE2ZJAQgghhBDn1d69e/n222+xWq0AaLVarrnmGvr16yd7QwhxEZNAQgghhBDnhd1uZ82aNWzbtk0t8/f359Zbb6VFixZN2DMhRGOQQEIIIYQQjS47O5vly5eTnZ2tlnXq1IkbbrgBs9nchD0TQjQWCSSEEEII0WgURWH79u38+OOPOBwOAPR6PSNHjuTKK6+UqUxCXEIkkBBCCCFEo7BYLKxatYo9e/aoZaGhoYwdO5aQkJAm7JkQ4nyQQEIIIYQQ5ywtLY3ly5dTWFiolvXo0YPhw4djMBiarmNCiPNGAgkhhBBCnDVFUfj9999Zt24dLpcLALPZzI033kjHjh2buHdCiPNJAgkhhBBCnJXS0lJWrlzJkSNH1LLo6GjGjBmDv79/03VMCHFBSCAhhBBCiDN2+PBhVq5cSVlZmVp29dVXM3jwYLRabRP2TAhxoUggIYQQQogGczqdrFu3jt9//10t8/b25pZbbiE+Pr4JeyaEuNAkkBBCCCFEgxQUFLB8+XLS09PVstatW3PzzTfj5eXVhD0TQjQFCSSEEEIIcVp79uzhu+++w2q1AqDVarn22mvp27ev7A0hxGVKAgkhhBBC1Mlut7N69Wr+/PNPtSwgIIBbb72VqKioJuyZEKKpSSAhhBBCiFplZ2fz1VdfkZOTo5Z17tyZG264AZPJ1IQ9E0I0BxJICCGEEMKNoihs27aNNWvW4HA4ADAYDIwcOZJu3brJVCYhBCCBhBBCCCGqqaio4LvvviMpKUktCwsLY+zYsQQHBzdhz4QQzY0EEkIIIYQA4NixYyxfvpyioiK1rFevXgwbNgy9Xh4ZhBDu5KeCEEIIcZlTFIXffvuNdevWoSgKAGazmdGjR9O+ffsm7p0QormSQEIIIYS4jJWUlLBy5UqSk5PVspYtWzJmzBj8/PyasGdCiOZOAgkhhBDiMnXo0CFWrlxJeXm5WjZw4EAGDRqEVqttwp4JIS4GEkgIIYQQlxmn00lCQgKJiYlqmY+PD7fccgtxcXFN2DMhxMVEPm4QQgghLiMFBQV89NFHbkFEmzZtuPfeeyWIEKIO77zzDrGxsZjNZvr06cPmzZvrrLtnzx5uvfVWYmNj0Wg0zJ8//5zbbK4kkBBCCCEuE7t27WLBggUcP34cAK1Wy/Dhw7n99tvx8vJq4t4J0Tx98cUXPPbYY8yaNYs///yTrl27Mnz4cLKzs2utX15eTnx8PC+//DLh4eGN0mZzJYGEEEIIcYmz2Wx88803rFixApvNBkBgYCBTpkzhqquukg3mhKjHvHnzuOeee5g8eTIdO3ZkwYIFeHp68tFHH9Vav1evXsyZM4cJEybUuQP8mbbZXEkgIYQQQlzCsrKyWLhwITt27FDLunTpwtSpU4mMjGy6jglxEbDZbGzbto2hQ4eqZVqtlqFDh7pND2zqNpuKJFsLIYQQlyBFUdi6dStr1qzB6XQCYDAYGDVqFF27dm3i3gnRtEpKSiguLlZfm0ymWkcPcnNzcTqdhIWFuZWHhYWxb9++s7r2+WizqUggIYQQQlxiKioq+Pbbb90eSsLDwxk7dixBQUFN2DMhmoeOHTu6vZ41axazZ89ums5cxCSQEEIIIS4hqampLF++3O3T1t69e3Pdddeh18uvfSEA9u7dS1RUlPq6rlyG4OBgdDodWVlZbuVZWVl1JlKfzvlos6lIjoQQQghxCXC5XGzYsIFFixapQYSHhwcTJkxg5MiREkQIUY2Pjw++vr7qV12BhNFopEePHiQkJKhlLpeLhIQE+vbte1bXPh9tNhX5qSKEEEJc5EpKSlixYgUpKSlqWUxMDGPGjMHX17fpOibEJeCxxx5j0qRJ9OzZk969ezN//nzKysqYPHkyAHfeeSdRUVG89NJLQGUy9d69e9W/p6ens2PHDry9vWndunWD2rxYnFUgER8fz5YtW2rMsywsLKR79+4cOXKkUTonhBBCiPodOHCAb775hvLycgA0Gg0DBw5k4MCBaLUy8UCIczV+/HhycnKYOXMmmZmZdOvWjdWrV6vJ0qmpqW7/1o4fP86VV16pvp47dy5z585l0KBBrF+/vkFtXizOKpBISUlRV4Cozmq1kp6efs6dEkIIIUT9HA4HCQkJbNy4US3z8fHh1ltvJSYmpgl7JsSlZ/r06UyfPr3WY1XBQZXY2FgURTmnNi8WZxRIfPvtt+rf16xZg5+fn/ra6XSSkJBAbGxso3VOCCGEEDXl5+fz1VdfkZGRoZa1bduW0aNH4+np2YQ9E0JcTs4okLj55puBymHTSZMmuR0zGAzExsby2muvNVrnhBBCCOFu586dfP/99+oO1Tqdjuuuu47evXvLDtVCiAvqjAIJl8sFQFxcHFu2bCE4OPi8dEoIIYQQ7mw2Gz/88AN//fWXWhYUFMStt95KREREE/ZMCHG5OqscieTk5MbuhxBCCNFkHA4H69atY+fOnVgsFsLCwhgyZAitWrU67bnFxcWsWbOGw4cPoygKcXFxDB8+nICAALXOjh07+Oabb+ps45ZbbqFLly51Hs/MzOSrr74iLy9PLevatSvXX389RqOxgXcphBCN66yXf01ISCAhIYHs7Gx1pKLKRx99dM4dE0IIIS6Ub775hr1799KnTx+CgoLYsWMHn3/+OZMmTaJly5Z1nmez2Vi8eDFWq5Wrr74arVbLxo0bWbRoEffee6+arxATE8Mtt9xS4/yNGzeSmZlJfHx8re0risLmzZtZu3atusiJ0Whk1KhR9QYeQgixefNmevTogU6nq/W41Wrlm2++Ydy4cWd9jbNaF+65555j2LBhJCQkkJubS0FBgdtXY0pPT+fvf/87QUFBeHh4cMUVV7B161b1uKIozJw5k4iICDw8PBg6dCgHDx50ayM/P5+JEyfi6+uLv78/U6ZMobS01K3Ozp07ufrqqzGbzURHR/Pqq6826n0IIYRontLT09m9ezfXXnstw4YNo0ePHkyaNAk/Pz/Wrl1b77lbtmwhPz+f22+/nf79+9O3b1/uuOMOSkpKSExMVOsFBATQpUsXt68OHTqQl5dHXFwc3t7eNdouLy/niy++YPXq1WoQERERwdSpUyWIEEKcVt++fd1GMX19fd22aCgsLOT2228/p2uc1YjEggULWLRoEXfcccc5Xfx0CgoK6N+/P0OGDOHHH38kJCSEgwcPug0Xv/rqq7z55pssXryYuLg4nn32WYYPH87evXsxm80ATJw4kYyMDNauXYvdbmfy5MlMnTqVzz//HKgclh42bBhDhw5lwYIF7Nq1i7vvvht/f3+mTp16Xu9RCCFE09q7dy8ajYYePXqoZXq9niuvvJKff/6ZoqIit1UKTz03MjKSqKgotSw4OJj4+Hj27NnDtddeW+d1Dxw4gM1m44orrqhx7OjRo6xYsULdoRrgqquu4tprr5UdqoUQDXLqErS1LUnbkGVq63NWP41sNhv9+vU7pws3xCuvvEJ0dDQff/yxWhYXF6f+XVEU5s+fzzPPPMPo0aMB+OSTTwgLC+Prr79mwoQJJCUlsXr1arZs2ULPnj0BeOutt7j++uuZO3cukZGRLFmyBJvNxkcffYTRaKRTp07s2LGDefPmSSAhhBCXuMzMTIKCgjCZTG7lVcFBZmZmrYGEoihkZWW5bTxVJTIyksOHD2O1Wmu0W2XXrl3o9Xo6dOiglrlcLjZs2MCGDRvUX/Cenp6MHj2atm3bnvU9CiFEbc51pbezmtr0j3/8Q/00/3z69ttv6dmzJ7fddhuhoaFceeWVLFy4UD2enJxMZmYmQ4cOVcv8/Pzo06ePOqScmJiIv7+/GkQADB06FK1Wy6ZNm9Q6AwcOdEtYGz58OPv3769zqpbVaqW4uFj9KikpadR7F0IIcWGUlJTg4+NTo7yqrK6f7xUVFTidzlqnJTXk3EOHDtGuXTs10CguLuaTTz7hl19+UYOI2NhY7r33XgkihBDN0lmNSFgsFt5//33+97//0aVLFwwGg9vxefPmNUrnjhw5wnvvvcdjjz3Gv/71L7Zs2cJDDz2E0Whk0qRJZGb+//buPD6q6v7/+OvOmn0jJCEQ1kQWQVbB4MIWWYpWKnVBtGqpVgtdpF9t7ddq1e9XazdXrK1Vqb+vitpqrajUyCZqZFNA9n0LSYBA9mTW+/tjkoEhCRBIMgm8n4/HVO+95557rjmdmc/c8zmnEKDecuKpqanBY4WFhaSkpIQct9lsJCUlhZQ5/knH8XUWFhaGDKWq8/jjj/Pwww83y32KiJyTHq7/Bbst8nIH1kNH4OEfhey3EQ/cifeDn8MHq+ud5yEWuAvbkodhyYoTzu0PTMI7ZzhwsN65G7kIHxMYsOFR2HAbW+jFe0ykmkBytoGf0XzOZbv/gOXJmc10p83ooYpTlxGRsNu4cWPw+65pmmzevDmYJ3z48OGzrv+MAol169YxaNAgANavXx9yrDkXw/H7/QwbNozHHnsMgMGDB7N+/XpeeOGFegvitbb777+f2bNnB7fz8/Pp169fGFskIiJnwoYHH/VnNfHWfkTa8DZ4nr12v/ek53oaPPcb+hFJNd3ZywLGsJxjT83jKGMq8+lKftNuRETkBOPGjQvJg7jqqquAwPd10zTP+nv7GQUSixcvPquLnq5OnTrV+3Let29f/vnPfwKQlpYGQFFRUchiPEVFRcFAJy0tjYMHQ38N8nq9HDlyJHh+WloaRUVFIWXqtuvKnMjpdIaMez0+IU5ERNqPWCopo/7Tk3Kia483/Ot7JNVY8VJRW+50zy0llj104UI2M5dpFHLsqXoftvFtFhBJzRndi4hIndZY961NT/1w6aWXsmXLlpB9W7dupVu3bkAg8TotLY2FCxcGA4eysjKWL1/O3XffDQSmviopKWH16tXBGTkWLVqE3+9nxIgRwTL//d//jcfjCQ7Tys3NpXfv3g0OaxIRkXNHKgfZRVdcOHDiDu7PJ/ADVVoDQ5MADCCVQxyg/g9O+aSTSAnOBp5IfENfwGAzmfgIfOZY8TKBxQxjDc33XF9Ezmd135db0hkFEmPGjDnpo5BFixadcYOOd8899zBy5Egee+wxrr/+elasWMFf//pX/vrXvwKBxzI/+9nP+J//+R+ysrKC07+mp6czZcoUIPAEY+LEidxxxx288MILeDweZs2axY033kh6ejoAN910Ew8//DAzZszgF7/4BevXr+fpp5/mySefbJb7EBGRtqsfW8ljOKsZyEhWAoHhSmsYQGcOEE8gYbqUWDzYSeZI8Ny+bGUhozhAKukEnmQfJpFddA3WdTwXdj4n8CNWXRCRTDFTeZ80DrXofYrI+aukpISXXnqJTZs2AdCvXz9mzJjR6NTWp+uMAom6X//reDwe1qxZw/r165s1d+Hiiy/m3Xff5f777+eRRx6hR48ePPXUU0yfPj1Y5r777qOyspI777yTkpISLrvsMhYsWBBcQwLgtddeY9asWYwbNw6LxcLUqVN55plngsfj4+P5+OOPmTlzJkOHDiU5OZkHH3xQU7+KiJwHulBAPzazkMupJIokjrKW/pQQx7dZECz3Lt9iD115iN8H913M13zFRbzOVLJZiRU/eQwjhkqyTwgkCkhhHlOo4djn0yDWMYlFOBrJpRAROVurVq1iwoQJREZGMnz4cACefPJJHnvsMT7++GOGDBlyxnUb5tmuRHGc3/zmN1RUVPCHP/yhuapsN/bv309GRgb79u2jS5curXLN7r/8oFWuI61v928nh+W66lPnpnD1p/YyaxMEnkAs4jK+oR/VRJDKIcbwGZnsDpaZyw31AgmAMmL4D2PZQXdMDLqzlwksJokSAExgOUPJZRT+2sRsG26+zX8YwOZWusNmplmbpJ0Kx/e1cLv88svJzMzkxRdfDC5o6fV6+cEPfsDOnTv59NNPz7juZs2RuPnmmxk+fPh5GUiIiEj7ZcPHeJYynqWNlrmNNxvcH0cF1/HvBo9VEcl7TGQrmcF96RQwlfnBQENEpCWtWrUqJIiAwFII9913X8g6a2eiWQOJvLy8kCFFIiIi56vdZPAOkynn2GJ32axgHMuw4g9jy0TkfBIXF8fevXvp06dPyP59+/Y1uBhnU5xRIHHttdeGbJumSUFBAatWreLXv/71WTVIRESkvfBjsJculBNNLJV0ZT8ASxnJp2RD7RxMUVQyhY/IouWnYxQROd4NN9zAjBkz+MMf/sDIkSMB+Pzzz7n33nuZNm3aWdV9RoHEiRneFouF3r1788gjjzB+/PizapCIiEh7sIksFjCWMuKC+2KoIIIaDpMc3NeDPXyHD4ilMhzNFJHz1K5du+jRowd/+MMfMAyD733ve3i9XkzTxOFwcPfdd/Pb3/72rK5xRoHEK6+8clYXFRERac82kcVbXFNvfwXRVNQubmfgZwyfcSkrsNBs85qIiJyWXr160a1bN8aMGcOYMWPYvn07JSUlwWNRUVFnfY2zypFYvXp1cD7aCy+8kMGDB591g0RERNoyPwYLGFu7deKaSkbt//q5lXl0I79V2yYiUmfRokUsWbKEJUuW8MYbb+B2u+nZsydjx45l7NixjB49mtTU1LO6xhkFEgcPHuTGG29kyZIlJCQkAIGFLsaMGcO8efPo2LHjWTVKRESkrdpLl5DhTA0xsWBiaaUWiYjUN3r0aEaPHg1ATU0NX3zxRTCw+Pvf/47H46FPnz5s2LDhjK9xRu9yP/7xjykvL2fDhg0cOXKEI0eOsH79esrKyvjJT35yxo0RERFp68qJbtZyIiItLSIigrFjx/LAAw/w8MMP85Of/ISYmBg2bz67tWzO6InEggUL+OSTT+jbt29wX79+/ZgzZ46SrUVEpF2o8hr8vx0dWH80gg0lkZR5rDw0qICrM0pDyr27J56P9sezu8JBuddCj7Q4eg89df335yUS7+7O7VnF5KSX1zv+n/xYXt3egV0VDqJsfq5IreAnfQ+R4PQ11y2KyHnO7Xbz5ZdfsnjxYpYsWcLy5cvJyMjgiiuu4LnnnmPUqFFnVf8ZBRJ+vx+73V5vv91ux+/X3NgiItL2lbhtvLg1mbRID1lxNawubvgJwpbSCNKjPFyRVkGs3Ud+1VEKPCOxNfA5CIEp0a3eSm5J28zCA7H8cnVn/sd/gIldyoJl/rE7gd9+k8bw5EruufAgB6ttvLEriU2lEcy9bA9Oq5KzReTsjB07luXLl9OjRw9GjRrFD3/4Q15//XU6derUbNc4o0Bi7Nix/PSnP+WNN94gPT0dgPz8fO655x7GjRvXbI0TERFpKclOLwuu3EZyhI+NJRF8b1nDgcQvLyoK2f6a/vybxoMIA/iu/RP69jjKdd2Pcttn3XhyYwrj0suwW8DjhzmbOzIkqYo5l+zDqM3XviipmntWZPDu3gRu7HG0OW9VRM5Dy5Yto1OnTsHE6lGjRtGhQ4dmvcYZ5Ug899xzlJWV0b17d3r16kWvXr3o0aMHZWVlPPvss83aQBERkZbgsJokRzRtGNF2uvM+E47t8LlCjrtqqrneeI++bAPAYsCV6eUUu2ysLg5MtbijzEm5x8qVncuCQQTA5amVRFl9fJx/divNiohAYCKkv/71r0RFRfHEE0+Qnp7OgAEDmDVrFv/4xz84dOjQWV/jjJ5IZGRk8NVXX/HJJ58EkzT69u1LTk7OWTdIRESkLdrqSeMftmswjcBvcDt37uSHsQtI75hEOdG8vDGKbQdK6ZuzI+S8CGtgyO/mkggu6ViF2x+IHpyW+sOXnFaTLaUR+M1AECIicqaio6OZOHEiEydOBKC8vJzPPvuMxYsX87vf/Y7p06eTlZXF+vXrz/gaTXoisWjRIvr160dZWRmGYXDllVfy4x//mB//+MdcfPHFXHjhhSxbtuyMGyMiItIWlRDHXN91eAwHAIcK85loLGZkx0q6s48BbCbdv5+D1TYKqkJ/o/u69knEwZrAcKiu0R4MTNYejQwpt7vCwVG3DZffQplHU8eKSPOKjo4mKSmJpKQkEhMTsdlswfXgzlST3qmeeuop7rjjDuLi6s+fHR8fzw9/+EP+9Kc/nVWDRERE2pJqIvg/voszIgKAiOpCSrZ+icsbWm5K11IsBvxydWfWHolkf6WdV7YlsaQwsNK1q/ZJRILTR056OfP3xfN/O5LYX2nn6+JIfrU6HZsReErh8imQEJGz4/f7WbFiBb/73e+YNGkSCQkJjBw5kueff560tDTmzJnDzp07z+oaTRratHbtWp544olGj48fP54//OEPZ9UgERGRtsKDjTf4DsUEEhSTKeb7kW9TPNTHDUt6EGnzc0OPEgCy4lz8z5ADPL4ulRmfdwOgg9PL7AsP8ttv0oiyHpvV8L8vKsTlM3hqYwpPbUwBYFLnUjpHeVhcGEuUTTMgisjZSUhIoLKykrS0NMaMGcOTTz7J6NGj6dWrV7Ndo0mBRFFRUYPTvgYrs9maJXFDREQk3PwYvMtk9tEFgBgqmM4/iKSGLtHQO76GBfnxwUACICe9nFFp5WwtC+Q59ImvYfXhwNCmrjHuYLkYu58/Dc+nsMrGgWo7nSI9dIry8v3PupLo8BJrVyAhImfn97//PWPGjOGCCy5osWs0KZDo3Lkz69evJzMzs8Hj69ata9a5aUVERMLBBP7DGDYR+AC24+Ym/kkCx9aCqPFZ8PjrZ0TbLXBhQk1we/nhwLSyw5Mr65VNi/KSFhUYI1XusbCpNIKxneovXici0lQ//OEPW/waTRqE+a1vfYtf//rX1NTU1DtWXV3NQw89xFVXXdVsjRMREQmHLxnGCgLLVxumn+t5j04cDB5ffzSCHeVO+ibU/zw83t4KO+/sSeDy1Aq6xXhOWva5TR3x+Q1u6qk1JESkfWjSE4kHHniAd955hwsuuIBZs2bRu3dvADZv3sycOXPw+Xz893//d4s0VEREpLm9uSuBco+VwzWBj8NPC2PYZc3iaPqYYJkN36zldX8NPWMTibSabC938v6+eGJsfn6QdTikvusW9yAnvZzUSA8Hquz8c3cicXY/9w8oDCk3d1sSO8qdXJhYg80wWVIYw5eHYri796GQpxkiIm1ZkwKJ1NRUvvjiC+6++27uv/9+TDMwu4RhGEyYMIE5c+aQmpraIg0VERFpbv+3owMF1cdy/9a6uxGZegnW2u3L/Z8TY9nOqqNRLCyIxeWz0DHCy4TOZczIKiY9KvQpQ1aci3/vjeeI20qCw0dOehk/7H2YJGfowneZcS4WF8byaVEMPtMgK87Fb4fmk5OuYU0i0n40eUG6bt268eGHH3L06FG2b9+OaZpkZWWRmJjYEu0TERFpMe8ft3jcITrwMldSUxtGDGIdYyxfMLb/6df32NADp1XustRKLkutnzMhItKenNHK1gCJiYlcfPHFzdkWERGRsCgnmteYSg2BtSIy2clV5KLFpUVEGqcVb0RE5LzmwsFrfJdS4gHoRCHX8W+saApWEQmYM2cO3bt3JyIighEjRrBixYqTln/77bfp06cPERERDBgwgA8//DDk+G233YZhGCGviRMntuQttAgFEiIict7yYeEtrqGIwKJwCZRwE+/g4OQzLInI+ePNN99k9uzZPPTQQ3z11VcMHDiQCRMmcPDgwQbLf/HFF0ybNo0ZM2bw9ddfM2XKFKZMmcL69etDyk2cOJGCgoLg64033miN22lWCiREROS8ZALvM4GddAcgkmqm8w9iUO6CiBzzpz/9iTvuuIPbb7+dfv368cILLxAVFcXLL7/cYPmnn36aiRMncu+999K3b18effRRhgwZwnPPPRdSzul0kpaWFny1x3xjBRIiInJeWsylrCWQSW3Fy428SzJaw0HkfFBeXk5ZWVnw5XK5GizndrtZvXo1OTk5wX0Wi4WcnBzy8vIaPCcvLy+kPMCECRPqlV+yZAkpKSn07t2bu+++m+Li4rO8q9anQEJERM47q7mIZYys3TK5lg/oSn5Y2yQiradfv37Ex8cHX48//niD5Q4fPozP56u3vEFqaiqFhYUNnlNYWHjK8hMnTuTVV19l4cKFPPHEEyxdupRJkybh8/lOrK5NO+NZm0RERNqjrfTkA64Mbk9kEf3YGsYWiUhr27hxI507dw5uO53OVr3+jTfeGPz3AQMGcNFFF9GrVy+WLFnCuHHjWrUtZ0NPJERE5LyRTxr/4GrM2o+/bFYygq/C3CoRaW2xsbHExcUFX40FEsnJyVitVoqKikL2FxUVkZaW1uA5aWlpTSoP0LNnT5KTk9m+fXsT7yS8FEiIiMh54QgJvM61eHAAcCGbuZIl4W2UiLRpDoeDoUOHsnDhwuA+v9/PwoULyc7ObvCc7OzskPIAubm5jZYH2L9/P8XFxXTq1Kl5Gt5KFEiIiMg5r4pIXmMqVUQD0I19TOFDLTgnIqc0e/ZsXnzxRf7+97+zadMm7r77biorK7n99tsB+N73vsf9998fLP/Tn/6UBQsW8Mc//pHNmzfzm9/8hlWrVjFr1iwAKioquPfee/nyyy/ZvXs3Cxcu5JprriEzM5MJEyaE5R7PlHIkRETknObBxhtcyxGSAOjIYW7gXWy0r6RGEQmPG264gUOHDvHggw9SWFjIoEGDWLBgQTCheu/evVgsx36bHzlyJK+//joPPPAAv/rVr8jKyuJf//oX/fvXzhJntbJu3Tr+/ve/U1JSQnp6OuPHj+fRRx9t9VyNs6VAQkREzll+DN7hKvaTDkAMFUznH0TS8FSPIiINmTVrVvCJwomWLFlSb991113Hdddd12D5yMhI/vOf/zRn88JGQ5tEROScZAILGMtmsgBw4GI6/ySe8vA2TETkHKFAQkREzklfMJyVDAHAgo/reY80Doa5VSIi5w4NbZJWYcHPENsBetmKceDlqBnFV550DvjjT3luFG6G2/eRbi3DwKTQH8dyTwYV5rFxhNGGmyzrYbpYS4gzXJgYHPVHsNabToE/riVvTUTaoG/oyyeMCm5/m//Qiz1hbJGIyLlHTySkVVxu382FtiJ2eJNY7umKacKVju2kWE4+xMCGj4nOLaRZy1nnTeNrTzpJRhXfcmzGiTdYrqulhAG2Qsr8EXzl6cxaTyfshp+Jzq1kWg+39O2JSBuyiwz+xaTg9hiWMZANYWyRiMi5SYGEtLhko4KetiOs9nZmlTeDrb6OLHD3psJ0cLFt/0nP7WM7SLzFxSeuLNZ7O7HRl8bH7iwiDQ8X2o4tNV/gj+WtmgF86unJZl8KG32pfODqQ4k/giG2/Ja+RRFpI4pI5k2+gx8rAENYy+V8GeZWiYicmzS0SVpcd+tR/CZs8XYM7vNhYasvmWH2fKINN5Wmo9FzD/mjOGxGB/eVmpEU+OPoYT3KV94uAJSYkfXO9WNhvy+e/vYibPjw1n6xEJHW0b36zVa9XhRurnJuItriAWCfL5657u/zY2a0ajvOB7vD3QARaRP0REJaXJKlijIzAs8JX+QP+wPBQZJR1ciZJolGdbDc8Q75o4mzuE45D3yk4cFjWvCpq4uc0+x4udK5LRhEHPJHscTdE1NLzomItBh9u5IWF2V4qDLt9fbX7YsyPA2e58SLzTCpbuDc6lOcCxBr1NDNepQ9vkR9mRA5h1nwM9axgyRLNQBlfiefuLL0FFJEpIUpkJAWZ8XE38AX+bqnBFb8DZ5nM8zaco2fa2vkXCs+xjh24MPCKk/nM2q3iLQHJpfZd5NuDUzcUGPayHVnUUP9HyBERKR5KUdCWpSlqhjTLMdZXUiHL9/CFxGPK20A1d1GYrUFggDHni9J3vxV/ZNTe8PwC7Bi1jtUF3zEfvEcfqIpyZ4ZPGb4XIxlHQmYfLlqLZGHPsQZmURNl6HUdB4GhuLn9spSVUz0jkXYSvZi8VSH9CesgTyb+FWvYC/ZXe9cd4dMygbfEty2lebjLFiD/egurNUl+O1ReOO7UNlrLP7o5MYb4feRsPzP2CoPUZk1nupulzb3bUoTDLHl08t2BACvafCJK5MyMyLMrRIROT8okJAWY6kpJWHFX3ENH4YzJp6KCyZiL91H9M7F2MoOEDXkKgCq3R58zjiqMnNCzvc7Y/CaJUQ2MHwpylcRONfj58QfHi+z7qRzBKzYms++mL6YCYOwF+8gZvMH2Er3U3HhtS1zw9Ki6vqTaYugJmM4fltkSH8qH3RTsGzD/Sk2ZDtyz2fYS/biSr2Q6phULK4KIvevIHHFXyi5+Af4YlIbbEfEvuVYa0qb/walyXpbDzLQHpi9zW/CUndPDpkxYW6ViMj5Q4GEtBhnwVos3hoORXalr6Mcf5fBVHQZBphEFKylo3kZAKUVNZi2CFydBtar46i5kWRLZb39ae58KrDiikrF8BxL1h5m20emvZIVFYls7HpxcH9Nl4sxN/yLiIKvqeoxCn9Uh+a/YWlRdf3p6LAZ+GJSAHAd158qPNWY9sDsXY31p+NVd82mvP9UsBx7G3Sl9Sfxy+eJ3P0ZFf2n1jvHcFcQtWspVd0uJXrn4ua7OWmyrpajXGLfG9xe7unKXn9iGFskInL+0RgPaTGG1wXALn8HLAb0th0CwO+IxbBYyXKUcNAfTbU78MQh2qwm3h/6S+8eXyIdLVV0MI4FE0ll2+kY62S3GRoM9LcVMsBexFpPGhusmfXa407pC4CtUgvUtUd1/cnvCJ3Fy++IxcTAtJyQWOv3Qe05DfEmdA0JIgD8UR3wRXfEVnmowXOit32CL6rDKYMUaVkdjQpGOXZhqU2fWudJY7MvJbyNEhE5D+mJhLQYT2J32PMZNd98zO7BAxnq3E90ZQFu4wBdRl1JjOHhM3cPAKxVxVxe/SnJHTrw3n8WU9N5CFU9RrPJm8IF1sNc6dzGem8afhMGxBdR47WxzuhJBF8AgV8nL7bvp9TvpMSMpKe1OKQtB3xxmO5AMqbfHtWa/xmkmdT1p5iN71HVawymPQpbyT4i9q+kJmNEMEcCAv2pw+L/xTB9+B0xwf7EicHGiUwTw12JL7pjvUO20v04C9ZQOkxrEoRTnFFDjnM7NiOQJ7XDm8RqryZUEBEJBwUS0mI8yVlU9hxL1O5lbFi4DW/v3vTs0gX7hf04QjS57s4U+WOJiUrEndQdX3Q84McT15moXZ9irSqmfMD1fOTqzXDHPgbaCjBML8WHy/g88hJcNjt1KZVJlsDwpniLi1GOXfXa8lF1L2r2fokvMhFvXHrr/UeQZnN8f3Ie3hLcX9X9CqoyxwW3fXX9KSYVw+fBUbQhpD+djLNwHVZXGVU9x4QeME2it3yIK7U/3oQMLNVHm/Xe5PRE4OFKxzYiDC8ABb5YPvN0B03vLCISFgokpEX5IxPwJHajIqUvy+1RfL1hK84Da6jsPYmajHgAKvpNAeBDE6gGBg3D3PRvIvJXU911H1XxGSxx98JwV5GY9wzV3S+nulvoWOg13s6sOcmvkjGb/k1E5SFKB00/9a/S0mbV9SdXSl9MexSOw1uJ3L0MvzMm8FSCY/2pjqvTwJD+5I3PaLBua+Uhojd/gCc+A1f6oJBjzoI12CoOUn7RDS1xW3IabPjIcW4jzhIYrnbUH8kidy/8GqErIhI2CiSkxTgKvyFm0/scHflj/BGBoMGd0i/w6+62XFypAzAdDQ8zqu46koj81diLdwa/+EXtWIhpi6S69gvj6Yrc/RkR+aup7DkWT/IFZ3dTEjbN3Z+OZ7jKiVvzGqYtgvKLrg+ZItjw1hC9/ROqu40MXldal4HJKMdOOtY+eaw07eS6s3DrI0xEJKz0U460mMj9K/HGptX78uXu2AfD78FWXtDoub6IOAAs3sBKtZaq4sAvyhkjsLjKsVQfDQwv8XvB9GGpPhoye1Md54Gvidr+CdWdh1Hdc1Qz3p20tubsT8czvDXEf/1/GN4aygbfjN8ZF3rdPV+A34crtX+w31lcZYFzPdXH+qG0EJNL7Hvpag1MxOA2reS6sqg0Hac4T0REWpp+zpEWY7grMG2R9Q+Yvtp/NrwqNYC1dgy63x6FgUm6v5ik9E64ildQvPWjeuWTPn+K6oxLqOw9KbjPcXAzMZv+jTulL5V9Jp/dzUjYNVd/CuHzELfmdaxVxZQOuTU4rezxLDWlWLzVJH45p96xqN3LiNq9jKMj7sIX2+n0b0ZO2wBbIX1qZ3zzmQaL3L04amrCBBGRtkCBhLQYf1QH7MU7sFQeDlkp2Fn4DSYGvthUDG8NpsUWOg2naRK161MA0tI7c7FzHdGRHug4FIBKr8HqIw72VduI2rEIw+ei8oJJ+KKSglXYju4mdv3beBK6BdYK0GrW7V5z9Cd3h+OmBTb9xH7zNrbSfZQNnIY3oeHcieqMEbg79gnZZ7grid38PjWdBuHu2Ad/pNYvaAm9rMUMs+cHtz/zdKfAH3eSM0REpDUpkJAWU9XtUuKLt5Ow+mWquwwPJsc6irdRkz4EvzMO+5FdxK7/B660AfgikzD8HhwHN2Mv3UvShZcyMr7+7DhRVpPLO7pY7O5Cyd7AL5N1a0QAWKpLiFv7BmDgTumHs2hDyPnemFR8sWkteu/S/M62P1V3HorvuBm7orf+B+fhLbiSe2PxVOMsWBtyvbq1Inxx6SHnAcFZm3wxKSF9T5pPJ0sZl9l3B7dXeTqz06eFJEVE2pJ29TPtb3/7WwzD4Gc/+1lwX01NDTNnzqRDhw7ExMQwdepUioqKQs7bu3cvkydPJioqipSUFO6991683tAxzUuWLGHIkCE4nU4yMzOZO3duK9zRuc2b2J3SYTPwxqYTuX8l0VsXYKk+QmWvcVT0uQoAX2QCnoRuOA5uInrbf4jasRj8Xir7XMWg7qkAGCfM7Fi3Pfy4VW2PZ60+isVbg+H3ELPlA2I3vBPych7c1GL3LC3nbPpTRZ+rqOxzdUh9tvJCAJyHt9TrI7Eb3mn1+5NjEo0qxjq2YzFMADZ5O/KNV8G/iEhb026eSKxcuZK//OUvXHTRRSH777nnHj744APefvtt4uPjmTVrFtdeey2ff/45AD6fj8mTJ5OWlsYXX3xBQUEB3/ve97Db7Tz22GMA7Nq1i8mTJ3PXXXfx2muvsXDhQn7wgx/QqVMnJkyY0Or3ei7xxnehbPDNjR73RyYGZsk5QZqljGjL1kbPMwyIMTxEDp9K4QlDHTxJPTic8/CZN1rarDPtTw0pHXb7GbfDH5moPtZCog0XVzq34ahdcG6PL4Hlnq5orQgRkbanXTyRqKioYPr06bz44oskJh4bi1xaWspLL73En/70J8aOHcvQoUN55ZVX+OKLL/jyyy8B+Pjjj9m4cSP/93//x6BBg5g0aRKPPvooc+bMwe12A/DCCy/Qo0cP/vjHP9K3b19mzZrFd7/7XZ588smw3K9ApOFp1nIi0vY58HKlYxvRtf+/PuiPZqm7B6aCCBGRNqldBBIzZ85k8uTJ5OTkhOxfvXo1Ho8nZH+fPn3o2rUreXl5AOTl5TFgwABSU1ODZSZMmEBZWRkbNmwIljmx7gkTJgTrkNZXbdqbtZyItG0W/Ix1bCfRUgNAmd/JJ65MfGgBSRGRtqrND22aN28eX331FStXrqx3rLCwEIfDQUJCQsj+1NRUCgsLg2WODyLqjtcdO1mZsrIyqquriYysP+Wky+XC5XIFt8vLy5t+c9KoIn8slX47UYanXo5EHdOEJKOKQmLRsAeR9szkcvtuOlkrAKg2bXzszsKFfigQEWnL2vQTiX379vHTn/6U1157jYiIiHA3J8Tjjz9OfHx88NWvX79wN+mcYmLUjosOBAwhx2q3DQNGOPZzuX0XVhpfQ0BE2rZhtv30tB0BwGNa+MSVSbnZtt7zRUSkvjYdSKxevZqDBw8yZMgQbDYbNpuNpUuX8swzz2Cz2UhNTcXtdlNSUhJyXlFREWlpgRk+0tLS6s3iVLd9qjJxcXENPo0AuP/++yktLQ2+Nm7c2By3LMfZ409ksbsXVScMX6o07ez2JgS3M21HmOzcTLThQkTal77WIgbYA++/fhOWuHty2IwJc6tEROR0tOmhTePGjeObb74J2Xf77bfTp08ffvGLX5CRkYHdbmfhwoVMnToVgC1btrB3716ys7MByM7O5n//9385ePAgKSmBVWtzc3OJi4sLPkXIzs7mww8/DLlObm5usI6GOJ1OnE5ncLusrOzsb1jq2eNPZK8rgVRLOZGGh2rTTpE/FhODbr4jXO7Yjd3w08FSxbedm1js7llvFicRaZu6Wo4ywr4vuP2lpxv7/Qnha5CIiDRJmw4kYmNj6d+/f8i+6OhoOnToENw/Y8YMZs+eTVJSEnFxcfz4xz8mOzubSy65BIDx48fTr18/brnlFn73u99RWFjIAw88wMyZM4OBwF133cVzzz3Hfffdx/e//30WLVrEW2+9xQcffNC6NywNMjEaDA72+JModUUyzrGdOIuLCMPLBMdWVnq6sNGXivImRNquFEsFoxw7gzlQaz2d2OLrGN5GiYhIk7TpoU2n48knn+Sqq65i6tSpXHHFFaSlpfHOO8cWk7JarcyfPx+r1Up2djY333wz3/ve93jkkUeCZXr06MEHH3xAbm4uAwcO5I9//CN/+9vftIZEO1BiRvK+qy/7fYFAw1KbN3GFfRdWfGFunYg0JM6oYZxjG7baBee2ezvwlTf9FGeJiEhb06afSDRkyZIlIdsRERHMmTOHOXPmNHpOt27d6g1dOtHo0aP5+uuvm6OJ0src2PjEncVgWz4D7YGZuHrZjpBgqWGRuxcVpvMUNYhIa4nEw3jHViKMQKCf74vlc0839ARRRKT9afdPJEQgMPzpK28XFrl64TED3bqDpYqrnZvoZFH+ikhbYMNHjnMbsZbAYqBH/JEsdmfi10eRiEi7pHdvOafs8Scy39WXMn/gKUSE4WW8YysX2goB8+Qni0iLMTAZ49hBsqUKgAq/g1xXFh4tOCci0m4pkJBzTl3exD5fPBDImxhuV96ESPiYZNv30MUaeDroMq3kurOowhHmdomIyNlQICHnJDc2FrozWevpFNzXq3a9iRitNyHSqgbaCuhtOwyAzzRY6M6kxGx4jR4REWk/FEjIOSuQN9H5hLyJauVNiLSiTOthhtgPBLeXeXpQ5I8NY4tERKS5KJCQc94efyLvu/pSqrwJkVaVbinlUvue4PYKTxd2+ZLC2CIREWlOCiTkvFBqRjJfeRMiraagoICxjh1YateK2OhNYYM3NcytEhE5M3PmzKF79+5EREQwYsQIVqxYcdLyb7/9Nn369CEiIoIBAwbUW4bANE0efPBBOnXqRGRkJDk5OWzbtq0lb6FFKJCQ80ZgvYlM1ihvQqRFlZSU8Prrr2M3/ADs9iWwwpOB1ooQkfbozTffZPbs2Tz00EN89dVXDBw4kAkTJnDw4MEGy3/xxRdMmzaNGTNm8PXXXzNlyhSmTJnC+vXrg2V+97vf8cwzz/DCCy+wfPlyoqOjmTBhAjU1Na11W81CgYScZwy+9nZmYb28iY3KmxBpBtXV1bz22mtUVFQAUOSL5lN3T0wFESLSTv3pT3/ijjvu4Pbbb6dfv3688MILREVF8fLLLzdY/umnn2bixInce++99O3bl0cffZQhQ4bw3HPPAYGnEU899RQPPPAA11xzDRdddBGvvvoqBw4c4F//+lcr3tnZUyAh56W99fImfMqbEDlLXq+XefPmcfhwYIamUr+The4sfPqoEZE2pry8nLKysuDL5Wp4ZILb7Wb16tXk5OQE91ksFnJycsjLy2vwnLy8vJDyABMmTAiW37VrF4WFhSFl4uPjGTFiRKN1tlV6d5fzlvImRJqPaZq8++677N27F4Do6Gg+dl+AC1uYWyYiUl+/fv2Ij48Pvh5//PEGyx0+fBifz0dqamiOV2pqKoWFhQ2eU1hYeNLydf9sSp1tld7h5bxWlzcx2HaAQfYCIJA3kWCpZpE7kwrTGeYWirQPH3/8MRs3bgTAbrdz00038dwzX4e5VSIiDdu4cSOdO3cObjud+rw/E3oiIXKSvIl0S2mY2ybS9uXl5fHll18CYBgG1113Henp6WFulYhI42JjY4mLiwu+GgskkpOTsVqtFBUVhewvKioiLS2twXPS0tJOWr7un02ps61SICFSq6G8iSsd2+ivvAmRRm3YsIGPP/44uH3VVVeRlZUVxhaJiDQfh8PB0KFDWbhwYXCf3+9n4cKFZGdnN3hOdnZ2SHmA3NzcYPkePXqQlpYWUqasrIzly5c3WmdbpaFNIscpNSN539WXKxy76GotxWLAxfb9dDCq+NzTDS/WcDdRpM3Ys2cP7777bnB71KhRDBkyJIwtEhFpfrNnz+bWW29l2LBhDB8+nKeeeorKykpuv/12AL73ve/RuXPnYJ7FT3/6U0aNGsUf//hHJk+ezLx581i1ahV//etfgcCT25/97Gf8z//8D1lZWfTo0YNf//rXpKenM2XKlHDd5hlRICFyAg82FrozGWQ7wODavImetXkTC5U3IQLAoUOHmDdvHj5fYGKCQYMGMWrUqDC3SkSk+d1www0cOnSIBx98kMLCQgYNGsSCBQuCydJ79+7FYjk2yGfkyJG8/vrrPPDAA/zqV78iKyuLf/3rX/Tv3z9Y5r777qOyspI777yTkpISLrvsMhYsWEBERESr39/ZMEzT1JiNZrB//34yMjLYt28fXbp0aZVrdv/lB61ynfNZV8tRLnfswlG7sJbLtLLE3ZMD/vgWve7u305u0foboz51bmru/lReXs5LL71EaWkgh6hXr15MmzYNqzX0iZ3607krXO9RImcrHN/XzmXKkRA5ib3+ROa7+lLiD/xC4AzmTRSgvAk5H7lcLl5//fVgEJGWlsZ1111XL4gQEZFznwIJkVMIrDfRh73HrTdxsT2f0fad2LTehJxHfD4fb7/9dnCe8/j4eG666SZNmygicp5SICFyGuryJr72dAru62E7ymTnZmKMhlfDFDmXmKbJ/Pnz2bFjBwARERFMnz6d2NjYMLdMRETCRYGEyGkzWFO73oS7dr2JJEs139Z6E3IeWLJkCWvWrAHAarUybdo0OnbsGN5GiYhIWCmQEGmixvImBihvQs5RX331FZ9++mlw+9prr6Vr165hbJGIiLQFCiREzsCxvIkEIJA3MUx5E3IO2rZtG/Pnzw9uT5gwgX79+oWxRSIi0lYokBA5Q4G8iV587UkP7qvLm4g1asLYMpHmceDAAd5++23qZgm/5JJLuOSSS8LcKhERaSsUSIicFYM13nQ+cWXiNgPTXyZZqrnauUl5E9KuHT16lNdffx2PxwPAhRdeyPjx48PcKhERaUsUSIg0g33+BN5X3oScI6qqqnjttdeorKwEoGvXrkyZMgXDMMLcMhERaUsUSIg0kzIzgvmuvsqbkHbN4/HwxhtvUFxcDEBycjI33ngjNpstzC0TEZG2RoGESDPyYGWhuxdfnZA3cZXyJqQd8Pv9vPPOO+zfvx+AmJgYpk+fTmRkZJhbJiIibZECCZFmZ7DWm07ucXkTibV5E52VNyFtlGmaLFiwgM2bNwPgcDi46aabSEhICG/DRESkzVIgIdJC9itvQtqRvLw8Vq5cCYDFYuH666+nU6dOpzhLRETOZwokRFpQXd7Entq8CaMub8KhvAlpO9avX09ubm5w++qrr6ZXr15hbJGIiLQHCiREWpgHK4tq8yZqp+Onh/UoVzk3KW9Cwm737t3861//Cm6PHj2aQYMGha09IiLSfiiQEGkVgbyJT9zH503UKG9CwurgwYPMmzcPny/wdGzIkCFcccUVYW6ViIi0FwokRFpRY3kTFylvQlpZWVkZr732Gi6XC4CsrCwmT56stSJEROS0KZAQaWUN5U0MteczRnkT0kpcLhevv/46ZWVlAKSnp/Pd734Xi0UfCSIicvr0qSESBg3lTXSvzZuIU96EtCCfz8dbb71FUVERAAkJCUybNg2HwxHmlomISHujQEIkbBrOm7jKuYlt27aFuW1yLjJNk3//+9/s3LkTgMjISG6++WZiYmLC3DIREWmPFEiIhFld3sTR4/ImXn/9dT799FNMU3kT0nwWLVrEunXrALDZbEybNo0OHTqEuVUiItJeKZAQaQNOzJsAWLx4MW+//XYwGVbkbKxatYrPPvssuH3ttdeSkZERxhaJiEh7p0BCpI3w1uZNrPakB/dt2rSJl156ieLi4jC2TNq7LVu28OGHHwa3J02aRN++fcPYIhERORcokBBpUwzWedO56aabcDqdABw6dIgXX3yRrVu3hrlt0h4lGxX84x//CA6TGzlyJMOHDw9zq0RE5FygQEKkDcrKyuKOO+6gY8eOQGC6zjfeeEN5E9IksUYNOc7teL1eAPr3709OTk6YWyUiIucKW7gbICIN69ChAzNmzOC9995j06ZNQCBvoqCggClTpgSfWIg0xImH8Y5tRBqBIKJ79+5cc801WnDuJCJ3LSV6xyK80SmUZM8MPej3ErnnC5wFa7HWlGDanHhj06noezX+iPiQclE7FuEsWIfFW403JpWqXuPwdOhV/4KnW6eISBulJxIibZjT6eS6665j7NixwX2bN2/mb3/7m/ImpFFWfOQ4txNnCSTqd+zYkRtuuAGbTb8dNcZSU0rUrmWY1gbW0/D7iFvzGlG7PsXTIZOK3pOp7nYpptWB4Q2dDCF2w7tE7s3D1WkAFRdMAsNC3Jr/w1ay54zrFBFpq/SpItLGGYbB5ZdfTlpaGv/85z9xuVwcPnyYF198kWuvvZYLLrgg3E2UNsTAZLRjFymWSgAqTTs/mz6diIiIMLesbYve9jGe+C4YponhqQo5Frk3D/vRPZQO+z7e+C6N1mEr3Y+zaD2VWeOp7nYpAK5OA0n88nmit+VSevEPmlyniEhbpicSIu1EVlYWd955Z728iaVLlypvQmqZjLDvpau1BAC3aSHXlUV8vIbJnIzt6G4cBzdS2XtS/YOmn4h9X+JO6RP4wu/3gc/dYD2OgxsxDQs1nYce22m1U5M+GHvpPiw1pU2uU0SkLdMTCZF2JCkpqV7exJIlSygsLFTehDDAVkhf2yEA/KbBIncmR82oMLeqjTP9xGz5kJr0IfhiUusdtlYewuoqpyYmlZhN/8Z5YA2G6cMbk0rlBZPwJPUIlrWVF+CL6oBpC336U/fEwVZeiDsivkl1ioi0ZXoiIdLOnCxv4vDhw2FsmYRTT2sxw+z5we3PPN0p8MeFsUXtQ8T+lVhqSqnqNbbB49aqIwBE7v0S+9HdVPS9mvJ+UzD8XuK+/n9YywuDZS2uCvyOmHp11O2zuMqaXKeISFumQEKkHarLm7jpppuCY98PHz7M3/72N603cR7qZCnjMvvu4PYqT2d2+DqEr0HthOGuImrnYqp7XIHpiG64TO2QI8PronTIrbjSB+NKH0zpkO8BELnn82Nl/R6w1H/Qb9bt83ubXKeISFumQEKkHWtsvQnlTZw/Eo0qxjp2YDUCf+/N3o58400Lc6vah6gdCzFtkVRnjGi0TF0Q4EnoGjIlqz8iAU9CV+yl+44raw8GC8cz6vbV1tWUOkVE2jIFEiLtXFJSEj/4wQ/o169fcN+SJUt48803cbk0jeS5LNpwc6VzGw7DB8BeXzxferoCWiviVCxVxUTkr6Y6YwQWVzmW6qNYqo8GAgHTh6X6KIanCr8zFqDBJxamIxrDUx3c9jtjsLgr6l+rdp/fGVf7z9OvU0SkLVOytcg5wOFw8N3vfpfPP/+chQsXArBlyxb+9re/ccMNN5CcnBzmFkpzc+DlSsdWog0PAIf80Sx198RUEHFarDVlGJjEbP0Itn5U73jS509RnXEJVb3GYhpWLK7yemUsrvKQYMAbm0bk0d0Y3pqQhGtb6f7gcQBfTOpp1ynHebh+/omcIx6qH4BL+6AnEiLnCMMwuOyyy5h+3JoBdXkTW7ZsCXPrpDlZ8DPWsYNESw0AZX4nua5MvFjD3LL2wxuTQtlFN9Z7eaNT8EXEU3bRjdR0HoJpc+JOzsJWug9r5aHg+dbKQ9hK9+FO6hnc5065EMP0E5G/+tiF/F4iDnyNJ65LcBhTU+oUEWnL9ERC5ByTmZnJHXfcwZtvvsnBgwdxuVzMmzePUaNGMWrUKAxDv1i3byaX2XfTyRr4NbvGtPGxOwsX9jC3q30xHdG4U/rW2x+590uAkGNVvcZhP7KTuNV/p6ZrIJ8iYu/yQH5F9yuC5bzxXXClXEjU9k8w3JX4IpOIKFiDpaaE8n7XhFzndOuU9m3V4Sjuyuva4LFXLtvNgMQaDlTZ+fbCXo3WMaVrCQ8MDMzk9ZuvOzF/f+PrwnyYs52UyPp5OiItRYGEyDno+PUmNm7cCMDSpUspLCzkO9/5jtabaMeG2vLpZQtMH+o1LeS6Mik3tWp1S/LFpFA69Hait+cStetTTAw8ST2ozBqPPyJ0it3yC79D1M54IgrWYnhr8MakUjZoOt7E7mdcp7R/N/Y4Qr+EmpB9GVGBYYmJDi+PDD5Q75y8g9F8lB/PJR0rg/uu7XaU4cdtA5gmPP5NGumRHgUR0uoUSIicoxrLm3jxxRe58cYblTfRDvWxHuQie+CXSb8JS9w9OWxq3HhzKh12e4P7fXHplA259dQVWO1UZU2gKmvCKYuedp3S7g1KqiYnvX5ODECkzeRbXcrq7Z+/L55om4/LU4/lD1yUVMNFSaEByZriSGp8FiY2UIdIS2vTORKPP/44F198MbGxsaSkpDBlypR6Y71ramqYOXMmHTp0ICYmhqlTp1JUVBRSZu/evUyePJmoqChSUlK499578XpDo/YlS5YwZMgQnE4nmZmZzJ07t6VvT6TFNZQ3UVxczIsvvqi8iXamq+Uol9j3Bre/9HRlnz8hfA0SkSap9Frw+k+v7OEaK6sORzG2UzlO68mn8l6QH4eBycTOCiTC7ciRI0yfPp24uDgSEhKYMWMGFRUnTyQ/ne+xhmHUe82bN68lb+W0telAYunSpcycOZMvv/yS3NxcPB4P48ePp7Ly2GO9e+65h/fff5+3336bpUuXcuDAAa699trgcZ/Px+TJk3G73XzxxRf8/e9/Z+7cuTz44IPBMrt27WLy5MmMGTOGNWvW8LOf/Ywf/OAH/Oc//2nV+xVpKXV5EykpKQC43W7mzZvHkiVLtN5EO9DRUsEox07q0lvWetLY4ksJb6NE5LQ9siaNUR9dwKUf9uaHX2SwseTkwxH/kx+HH+OUwYHXD7kHYrkosZr02qFSEj7Tp09nw4YN5ObmMn/+fD799FPuvPPOk55zqu+xdV555RUKCgqCrylTprTQXTRNmx7atGDBgpDtuXPnkpKSwurVq7niiisoLS3lpZde4vXXX2fs2LFA4D903759+fLLL7nkkkv4+OOP2bhxI5988gmpqakMGjSIRx99lF/84hf85je/weFw8MILL9CjRw/++Mc/AtC3b18+++wznnzySSZMOPXjaZH2oC5v4t///jcbNmwAjuVNTJkyJfjEQtqWOKOGHMd2bLULzm33JvGVt3OYWyUip8NuMRnbqYxLUypJcPjYVe7g/+1I4o7Pu/LSZXvoE9/wWj8L8uNIdnq4OLnqpPXnHYqm1GNjUpfDLdF8aYJNmzaxYMECVq5cybBhwwB49tln+da3vsUf/vAH0tPT651zOt9j6yQkJJCW1vYWG23TTyROVFpaCgS+EAGsXr0aj8dDTk5OsEyfPn3o2rUreXl5AOTl5TFgwABSU1ODZSZMmEBZWVnwy1ReXl5IHXVl6upoiMvloqysLPgqL2947KNIW+JwOJg6dSo5OTnB2Zvq1ps4fFgfRG1NBB7GO7YSYQSGYh7wxfK5pztacE6kfRiYVM3vhh3gmq6ljEqr4LasI8y9fA8AczZ1bPCcPRV2NpVGMr5zOZZT/F99QX4cNsMkJ13DmpqqvLw85Hvc2S7gmpeXR0JCQjCIAMjJycFisbB8+fIGzzmd77F1Zs6cSXJyMsOHD+fll19uM6MJ2k0g4ff7+dnPfsall15K//79ASgsLMThcJCQkBBSNjU1lcLCwmCZ44OIuuN1x05WpqysjOrqhlcYffzxx4mPjw++jl9VWKQtMwyDSy+9tMG8ia6Wo2FundSx4SPHuY1YixuAI/5IFrl74W8/b9si0oCMaA+j0ipYVRyFr4HvggvyA9O7TupcetJ6qrwGSwtjyU6pJMFxmskXEtSvX7+Q73GPP/74WdVXWFgYHD5cx2azkZSUFPy+2dA5p/oeC/DII4/w1ltvkZuby9SpU/nRj37Es88+e1btbS5temjT8WbOnMn69ev57LPPwt0UAO6//35mz54d3M7Pz1cwIe1Kr169uPPOO5k3bx4HDx7E7XYzzrmDrz2dWONNR796h4+ByWjHTjpaAsMaKv12cl1ZeNrPW7aInERqpAeP30K110KMPTQIWJAfR7doF30TTv4L+ZLC2MBsTacIOKRhGzdupHPnY8NEG5sW/Ze//CVPPPHESevatGlTs7btRL/+9a+D/z548GAqKyv5/e9/z09+8pMWve7paBefSrNmzQomrXTp0iW4Py0tDbfbTUlJSUg0V1RUFBxHlpaWxooVK0Lqq8uGP77MiRnyRUVFxMXFERkZ2WCbnE5nSKcrK9NjRWl/EhMT6+VNDLYX0MFSxafuHvriGhYm2fY9ZFgDXw5cppWP3RdQhSPM7RKR5pJfZcdp8RNlCw0i1h+NYF+lg7t6H2rkzGMW7I8jyupjVOrJZwWShsXGxhIXd+o1W37+859z2223nbRMz549SUtL4+DBgyH7vV4vR44caTS34XS+xzZkxIgRPProo7hcrrCvC9Wmn5GbpsmsWbN49913WbRoET169Ag5PnToUOx2e3COfAiM9967dy/Z2dkAZGdn880334T8cXNzc4mLiws+QcjOzg6po65MXR0i57Lj8yb8tY/Zu1pLudq5iXij4aF90nIushXQ2xbIV/GZBovcvSgxG/5BQ0TatqMua719W0udfFoYy4iOlfVyIBbkB77Ynmq2pqMuK8sPRzO6UwURtrYxVv5c1bFjR/r06XPSl8PhIDs7m5KSElavXh08d9GiRfj9fkaMGNFg3afzPbYha9asITExMexBBLTxJxIzZ87k9ddf57333iM2NjY4Xiw+Pp7IyEji4+OZMWMGs2fPJikpibi4OH784x+TnZ0dzHQfP348/fr145ZbbuF3v/sdhYWFPPDAA8ycOTP4B7jrrrt47rnnuO+++/j+97/PokWLeOutt/jggw/Cdu8irakub+LeD/Yw2rETp+Ej3uLiKucmlrl7sNefGO4mnhcyrYcZaj+2wu0yT3cK/VrlWKS9un91Ok6ryUWJ1SQ5vewsd/Lu3gQirH5+3Df0qYPPhNwDcQxIrKZL9Mmncv34QCw+02CS1o5oM/r27cvEiRO54447eOGFF/B4PMyaNYsbb7wxOGNTfn4+48aN49VXX2X48OGn9T32/fffp6ioiEsuuYSIiAhyc3N57LHH+K//+q9w3m5Qmw4k/vznPwMwevTokP2vvPJK8DHTk08+icViYerUqbhcLiZMmMDzzz8fLGu1Wpk/fz5333032dnZREdHc+utt/LII48Ey/To0YMPPviAe+65h6effpouXbrwt7/9TVO/ynnngD+ef7v6Mc6xnSRLNQ7DzzjnDtZ4OvG18iZaVLqllEvte4LbKz1d2OXrEMYWicjZGp1WwUf5cby+M4kKr4VEh5cxaeXc2fswGScECysORVPssnF7VvEp612QH0+Sw8vwjpWnLCut57XXXmPWrFmMGzcu+N30mWeeCR73eDxs2bKFqqpj0/qe6nus3W5nzpw53HPPPZimSWZmJn/605+44447WvXeGmOYbWX+qHZu//79ZGRksG/fvpA8jpbU/Zd6YnKu2v3byWG5bl2fsuHjUvseetqOBI/t9cWzzN0Dd9v+/aFdSjKq+JZzM3YjMF56ozeF5Z4MmitwC3d/knNPWPrUwzGtf01pHQ+1Xp5HOL6vncvadI6EiISHFytLPT1Y6ekSkjdxlfImml204eJK57ZgELHHl8CKZgwiREREWooCCRFphMF6bxq57ixqzEDCYLzFxdXOTVpvopk48DLesY0oIzDE4aAvmqXunpgKIkREpB1QICEiJ3XAH8/7rn4c8QdmDrLX5k0MtuUDGhl5pqz4GefYToKlBoBSv5NP3Jn49LYsIiLthAY7i8gpVZhOPnD1YaR9D71q8yYGHbfehPImmsrkcvsu0qyBccHVpo2P3Rfgwh7mdom0Xd2r3wx3E6SF7A53A+SM6acvETktXqx86unBiuPyJjKUN3FGLrbtp4ctMDzMY1rIdWVRYYZ/PnAREZGmUCAhIk1gsMGbxsfuC+rlTXRT3sRp6Wctor+9CAC/CUvcPSk2o8PcKhERkaZTICEiTVbgj+N9Vz+Kj8ubGOvcwRBbPobyJhrVzXKU4fZ9we08Tzf2+xPC1yAREZGzoEBCRM5IXd7EDm9ScN9AewHjHNtx4A1jy9qmFEs5Vzh2YtROyLTG04mtvo7hbZSIiMhZUCAhImfM10jexNXOTSQobyIo3qgmx7EdmxH4j7TN26F2pXAREZH2S4GEiJyl4/MmArM3xVlcXKW8CQAi8XClYxtOwwdAvi+Ozz3d0IJzIiLS3imQEJFmEcib6NtA3sT+8zZvwoaPHOc2Yi1uAIr9kSx298LUW6+IiJwD9GkmIs2m4byJQnIc2867vAkDP2McO0i2VAFQ4XeQ68rCgzXMLRMREWkeCiREpFk1lDfRxVp2nuVNmIy076WLtQwAl2nlY3cW1TjC3C4REZHmo0BCRFrA+Z03MchWwAW2wwD4TIOF7kxKzcgwt0pERKR5KZAQkRZT4I/j366+FPujgPMjbyLLepjB9gPB7U/dPSjyx4axRSIiIi1DgYSItKhK08kHrt7nRd5EZ0spI+27g9vL3V3Y7U9q/AQREZF2TIGEiLS4uryJ5e6MczZvooNRyRjHDiy1s7pu8Kaw0ZcW3kaJiIi0IAUSItJKDDb6UvlPg3kTR8LctrMTY7jIcW7DbvgB2OVLZIUnI8ytEhERaVkKJESkVRU2mDexk6HtNG/CiZcrHduIMgLDtAp9MSxz90ALzomIyLlOgYSItLrK2vUmth+XN3FRO8ybsOJnnGMbCZYaAEr8ESx0Z+LTW6uIiJwH9GknImHhw8KyRvImEo2q8DbuNBiYXOHYSaq1EoAq006uOws3tjC3TEREpHUokBCRMGo4b2KyczPd23TehMlw+z66W0sA8JgWPnFlUmE6w9ssERGRVqRAQkTCri5v4vBxeRNj2nDexIW2IvrZDgLgN2GxuxfFZnSYWyUiItK6FEiISJtQaTr50NWH7d4OwX1tMW+ih/UIw+37g9ufe7qT748PY4tERETCQ4N5RaTNCORNdOewP4rh9n1YjGN5E4vcvThqRoW1famWci637wpuf+VJZ7svOYwtEpE2w+sias/n2MrysZXmY/FWU95vCq70wfXLmn4i8lcRsX811qrDmFY73pg0Ki+YiC/22Pozlqpiord/gv3ITgy/D29sJ6p6jcWT1COkLmfBWhwHN2ErL8DiqcYXmYgrtT/V3UaC1d4KNy/nKz2REJE2xmBTY3kT1vDlTcQb1YxzbMdqBIZabfUms9bbKWztEZG2xeKpImrXUqyVh/DGpp60bMzG94je8hHeuE5U9P4WVT1G4Y+Ix+KuPFZfTSkJK/+GvWQv1d0upTJzHIbPTdzXr2I7uvtYZT4PsRv/hcVTSU2XYVT0nog3rjNROxcTt+b/wGx7w0Pl3KEnEiLSJtXlTYx17CDZUhXIm3Ds5BtPFau9nTFbcZ2GSNyMd2zDafgA2O+L4wtPV7RWhIjU8TtjKb78vzCdsdjK8nGs+GuD5RxF64koWEPZRTfiTunbaH2Ru5dheGsouWQmvujAk8+azkNJ/OI5YrYuoGTEXYGCFislw2bgTegaPNfVeRi+yASidy7GfmQnng69mu9GRY6jJxIi0mY1lDcxwF7Ila2YN2HHx5XObcRY3AAc9kex2N0LU2+fInI8iw3TGXvKYpF78vDEdQ4EEaYffO4Gy9lL9uKN7RQMIgCwOnB37B0YwlRVHLzu8UFEnbogxVp5qOn3InKa9ERCRNq0hvImOlvL+LZzIwvdmS2aN2HBzxjHdjpYqgEo9zv4xJWFF2uLXVNEzl2GtwZbWT41XS4mavsnROxbjsXnxheZSGVmDu7U/scK+71gi6hXh1mb82ArO4A7qkO943UsropANQ7NKCctR4GEiLQDgbyJI2YUYxw7iDS8xFrcTHZu5nNPd3b5kuqd4cDLMPt+ullLsOLnsD+alZ4upzVNa7JRQaatmB7WI0TUDmeqMa187L6AapS4KCJnxlJ1FAMTZ9F6MCxUZV2JaYsgYu+XxH7zD8qsTjzJWQD4opKxl+zB8LowbcfWqLGX7AXA6io76bUi93yG3+rE0yGz5W5Iznt6Ni8i7UaRP5b3Xf04dNx6E6MdOxlm23fCehMmOY5t9LQeYZM3hVWeLkQYHiY6txBn1JzyOl2spfS2HgoGEQAL3ZmUmfV/HRQROV1G7TAmi6eKsoHTqOkyHFfaRZQOuRXTHknUrk+DZWu6XIzFW0PsN29jLSvAUnmY6C0fYSs7ECjga3x4Z+SuT3Ec2UlVVg6mPbJF70nObwokRKRdqTQdfOTqw7aQvIkirnRsxVmbN9HdepRUayXL3N1Z401nsy+Fj1y9MTEYZDtwymt4TCuW2jzquglPDvpPPfZZROSkrIGBIL7IRLzxXY7ttzlxJ/fGVpYP/sAPGJ7kLCp6fwt7yR4SV7xAUt6zOA5vparXWABMm6PBSzgK1xO1YxE16UOo6TK8Ze9Hznsa2iQi7Y4PC595ulPsj67NmzDpbC3n6tq8ie6Wo1SZNvb4E4PnuLCz25dIT+sRLB4//kZ+R+liKWHYcQvOFfljSLNWtPg9ici5z1+bjN1Q3oLfEY1h+jD8HkxLIA+rJmMENemDsZUXYVqs+GLTcOZ/BYCvgfwIe/EOYje8EwhC+lzVgnciEqAnEiLSThls8qWwwH0B1bXrTcRa3Fzl3EyqtZwj/ihOnJ71kD8au+EnvpHhTclGJaMdO4NPI77xpHLU1LAAEWkefmccfkcMlpryescsrnJMiw3TesKTBqsDb0IGvrh0MCw4juzEtNjrzdRkK91P3Lp5eOPSKRtwPVg0KYS0PAUSItKunZg3YTP8RBleogz3CXkTUG0GEqUjDU+9emKNGnKc27AbfgB2ehNZ5e1Sr5yIyNlwpfbH6irFXrwjuM9wV+I4tBlPYg8wGv9qZivZi+PQJmrSB2MeN6OTtfIQcWtewxeRQNmg6VrNWlqNhjaJSLtXlzeRbd9Dli0wt3qSpYYrHVtZ6u6Fq/atzlf724kNf8j5Tjxc6dhGpBHIsSjwxbDM0wMtOCciTRGxbzmGpwaLO/DEwXFoC5aawOxKNV1HYNoiqOp+OY6iDcSue5Oartn4bU4i8ldhmH4qM3OCdVmqS4j95i3cHXvjd8RgqzxExP5V+GJSqTqunOF1EffV/8PwVOPqNhLH4a0hbfJFJuFNyGiFu5fzkQIJEWm3LPhxcmxmpdWezhz2R3GJfR+GQUjexFEzCmttAOE97mGsFR85zu3EW1wAHPVHsMid2WgOhYhIYyL3fIG1piS47Ty0CeehTQC4Ol2EaYvAdMZQOuz7RG/7mIi9eRimH098FyounIovNi14rmlz4nfGErlvBYanGn9EHNUZI6jucUXIdLCGpwqrqxSA6O2f1GtTTadBVCiQkBaiQEJE2q0USwWTnKG/vr1dM4Aqs5AIvFgNM5g38ZmnW/BJRJThpoe1mBrTTl9bESmWSgCqTDu57izcemsUkTNw9LJ7TqucPyqJ8oE3nrSMaY+kfOC0U9cVmcjhnIdP67oizU0/uYlIu3XEH8UC1wUhr2rTzkEzBjdWDvkDidI2w89oxy4G2Q5gmnC5Yw+jHbuY6NxKN2vglzy3aSHXlUWl6TzZJUVERBp05MgRpk+fTlxcHAkJCcyYMYOKipPP+vfXv/6V0aNHExcXh2EYlJSUNEu9rUWBhIi0W25sFPjjQl4+LOzxJRJpeNngSWPrcetNxFjqJ1lDYK2IDd7AytkiIiJnYvr06WzYsIHc3Fzmz5/Pp59+yp133nnSc6qqqpg4cSK/+tWvmrXe1qLn9yJyztntS+SgP5pLHXv4xpvKTm8CPawlGAYYjeRPZ1kPs9abjolBtOEi0xpI2u5gqQJgYO1CdhWmkx2++vO3i4jI+WvTpk0sWLCAlStXMmzYMACeffZZvvWtb/GHP/yB9PT0Bs/72c9+BsCSJUuatd7WoicSInLOMTHIdWWxy5dEP9shulpLGw0gIBBcxFg8pFoCM63EGi6G2A8wxH4gmD9Rt51lPdQatyAiIi2ovLycsrKy4Mvlcp1VfXl5eSQkJAS/7APk5ORgsVhYvnx5m6u3ueiJhIick9zY+NzTnc890MNazGjHrlOeU7e+RKE/jleqh52itIiItFf9+vUL2X7ooYf4zW9+c8b1FRYWkpKSErLPZrORlJREYWFhm6u3uSiQEJFzXt1CdM1VTkRE2reNGzfSuXPn4LbT2fBEG7/85S954oknTlrXpk2bmrVt7YkCCRE55xX5Y6n024kyPA0OcTJNqDTtFPljW79xIiLS6mJjY4mLiztluZ///OfcdtttJy3Ts2dP0tLSOHjwYMh+r9fLkSNHSEtLa+TMU2upepuLAgkROeeZGCz3dGWMYwemGZpwbZqBf67wdMXUStYiInKcjh070rFjx1OWy87OpqSkhNWrVzN06FAAFi1ahN/vZ8SIEWd8/Zaqt7ko2VpEzgt7/Iksdvei6oThS5WmncXuXuzxJ4apZSIi0t717duXiRMncscdd7BixQo+//xzZs2axY033hicWSk/P58+ffqwYsWK4HmFhYWsWbOG7du3A/DNN9+wZs0ajhw5ctr1hpOeSIjIeWOPP5G9rgRSLeVEGh6qa4cz6UmEiIicrddee41Zs2Yxbtw4LBYLU6dO5Zlnngke93g8bNmyhaqqquC+F154gYcfPrYy+RVXXAHAK6+8EhxSdap6w0mBhIicV0wMCv2nHhcrIiLSFElJSbz++uuNHu/evTtm3XjaWr/5zW9OOVvUqeoNJw1tEhERERGRJlMgISIiIiIiTaZAQkREREREmkyBhIiIiIiINJkCCRERERERaTIFEiIiIiIi0mQKJEREREREpMkUSJxgzpw5dO/enYiICEaMGBGy+qCIiIiIiAQokDjOm2++yezZs3nooYf46quvGDhwIBMmTODgwYPhbpqIiIiISJuiQOI4f/rTn7jjjju4/fbb6devHy+88AJRUVG8/PLL4W6aiIiIiEibYgt3A9oKt9vN6tWruf/++4P7LBYLOTk55OXl1SvvcrlwuVzB7dLSUgAKCgpavrG1vGWHW+1a0rr2798fluuqT52b1J+kuYWjT6k/nbtasz/VfU/z+/2tds1zmQKJWocPH8bn85GamhqyPzU1lc2bN9cr//jjj/Pwww/X2z98+PAWa6OcPzL+HO4WyLlE/Umam/qUNKdw9KeioiK6du3a+hc+xyiQOEP3338/s2fPDm57vV42bdpERkYGFotGjDW38vJy+vXrx8aNG4mNjQ13c6SdU3+S5qT+JM1Nfarl+P1+ioqKGDx4cLibck5QIFErOTkZq9VKUVFRyP6ioiLS0tLqlXc6nTidzpB9l156aYu28XxWVlYGQOfOnYmLiwtza6S9U3+S5qT+JM1Nfapl6UlE89FP57UcDgdDhw5l4cKFwX1+v5+FCxeSnZ0dxpaJiIiIiLQ9eiJxnNmzZ3PrrbcybNgwhg8fzlNPPUVlZSW33357uJsmIiIiItKmKJA4zg033MChQ4d48MEHKSwsZNCgQSxYsKBeAra0PqfTyUMPPVRvOJnImVB/kuak/iTNTX1K2gvDNE0z3I0QEREREZH2RTkSIiIiIiLSZAokRERERESkyRRIiIiIiIhIkymQkJMqLi4mJSWF3bt3N1pm7ty5JCQkNKle0zS58847SUpKwjAM1qxZc1btbGlLlizBMAwMw2DKlCnhbk67cTr9R2D37t3B/jVo0KBwN6dNU586PepTZ6+99bXu3bvz1FNPNemczz//nAEDBmC325kyZYr6jTSZAgk5qf/93//lmmuuoXv37o2WueGGG9i6dWuT6l2wYAFz585l/vz5FBQU0L9//9M674c//CG9evUiMjKSjh07cs0117B58+aQMnv37mXy5MlERUWRkpLCvffei9frPWm9R44cYfr06cTFxZGQkMCMGTOoqKgIHh85ciQFBQVcf/31TbrP892J/ae4uJiJEyeSnp6O0+kkIyODWbNmBRdfqrNkyRKGDBmC0+kkMzOTuXPnnvJa69at4/LLLyciIoKMjAx+97vfnVXb77rrLgzDqPfBfKq+0pCamhpmzpxJhw4diImJYerUqSGLX2ZkZFBQUMDPf/7zs2rz+eBk70nFxcV06dIFwzAoKSkJORauPnXbbbcFv5jVvSZOnBhSRn2qbWqor534tzQMg3nz5oWcF66+tnLlSu68884mnTN79mwGDRrErl27mDt3rvqNNJ0p0ojKykozLi7OzMvLa/a6n332WbNr165NPu8vf/mLuXTpUnPXrl3m6tWrzauvvtrMyMgwvV6vaZqm6fV6zf79+5s5OTnm119/bX744YdmcnKyef/995+03okTJ5oDBw40v/zyS3PZsmVmZmamOW3atHrlbr31VvOaa65pcrvPRw31nyNHjpjPP/+8uXLlSnP37t3mJ598Yvbu3Tvkv/XOnTvNqKgoc/bs2ebGjRvNZ5991rRareaCBQsavVZpaamZmppqTp8+3Vy/fr35xhtvmJGRkeZf/vKXM2r7O++8Yw4cONBMT083n3zyyZBjp9tXjnfXXXeZGRkZ5sKFC81Vq1aZl1xyiTly5Mh65R566CFz4MCBZ9Tm88Gp3pOuueYac9KkSSZgHj16NLg/nH3q1ltvNSdOnGgWFBQEX0eOHAkpoz7V9jTW1wDzlVdeCfl7VldXB4+3hfevpujQoYP58ssv19uvfiOnS4GENOrtt982O3bseMpyr7zyihkfHx/crnsDevXVV81u3bqZcXFx5g033GCWlZWZphn4YAWCr27dup1xG9euXWsC5vbt203TNM0PP/zQtFgsZmFhYbDMn//8ZzMuLs50uVwN1rFx40YTMFeuXBnc99FHH5mGYZj5+fkhZRVInL7T7T9PP/202aVLl+D2fffdZ1544YUhZW644QZzwoQJjdbx/PPPm4mJiSF/41/84hdm7969m9zu/fv3m507dzbXr19vduvWLSSQaEpfqVNSUmLa7Xbz7bffDu7btGmTCdT7kqIP75M7WZ96/vnnzVGjRpkLFy6sF0iEs0+d6j1DfaptaqyvAea7777b6Hnh7Gsnvl8B5osvvmhOmTLFjIyMNDMzM8333nvPNE3T3LVrV8jncF2AVEf9Rk6XhjZJo5YtW8bQoUPP6NwdO3bwr3/9i/nz5zN//nyWLl3Kb3/7WwCefvppHnnkEbp06UJBQQErV648o2tUVlbyyiuv0KNHDzIyMgDIy8tjwIABIYsITpgwgbKyMjZs2NBgPXl5eSQkJDBs2LDgvpycHCwWC8uXLz+jtsnp9Z8DBw7wzjvvMGrUqOC+vLw8cnJyQspNmDCBvLy8RuvJy8vjiiuuwOFwhJyzZcsWjh49etpt9vv93HLLLdx7771ceOGFDV6nqX1l9erVeDyekHvq06cPXbt2Pek9SX2N9amNGzfyyCOP8Oqrr2Kx1P9YC2efgsBQl5SUFHr37s3dd99NcXFxyHXUp9qek71/zZw5k+TkZIYPH87LL7+MedxyXOHuayd6+OGHuf7661m3bh3f+ta3mD59OkeOHAkOYYqLi+Opp56ioKCAG2644ayuJecnBRLSqD179pCenn5G5/r9fubOnUv//v25/PLLueWWW1i4cCEA8fHxxMbGYrVaSUtLo2PHjk2q+/nnnycmJoaYmBg++ugjcnNzg2/AhYWF9VYir9suLCxssL7CwkJSUlJC9tlsNpKSkho9R07tZP1n2rRpREVF0blzZ+Li4vjb3/4WPNbY37CsrIzq6uoG6zuTv3tDnnjiCWw2Gz/5yU8avU5T+0phYSEOh6PehASpqanqX03UUJ9yuVxMmzaN3//+93Tt2rXB88LZpyZOnMirr77KwoULeeKJJ1i6dCmTJk3C5/MF61Kfansae/965JFHeOutt8jNzWXq1Kn86Ec/4tlnnw0eD2dfa8htt93GtGnTyMzM5LHHHqOiooIVK1YEP38NwyA+Pp60tDQiIyPP6lpyflIgIY2qrq4mIiIiZN+FF14Y/BI/adKkRs/t3r07sbGxwe1OnTpx8ODBZmnX9OnT+frrr1m6dCkXXHAB119/PTU1Nc1StzSfhvpPnSeffJKvvvqK9957jx07djB79uxWbl19q1ev5umnn2bu3LkYhhHu5kgDGupT999/P3379uXmm28OU6tO7sYbb+Tb3/42AwYMYMqUKcyfP5+VK1eyZMmScDdNTqKx969f//rXXHrppQwePJhf/OIX3Hffffz+978PQwtPz0UXXRT89+joaOLi4prts1gEFEjISSQnJ9d7rPrhhx+yZs0a1qxZE/Ir8onsdnvItmEY+P3+ZmlXfHw8WVlZXHHFFfzjH/9g8+bNvPvuuwCkpaWFzFwCBLfT0tIarC8tLa3eG6vX6+XIkSONniOn1lD/qZOWlkafPn349re/zV/+8hf+/Oc/U1BQEDzW0N8wLi6u0V/MzuTvfqJly5Zx8OBBunbtis1mw2azsWfPHn7+858HZ205k76SlpaG2+2uN4tQUVGR+lcTNdSnFi1axNtvvx38m40bNy5Y9qGHHgLC16ca0rNnT5KTk9m+fXuwLvWptudk71/HGzFiBPv378flcgFtq69By34Wi4ACCTmJwYMHs3HjxpB93bp1IzMzk8zMTDp37hymlh1jBiYMCL6JZ2dn880334R8MOfm5hIXF0e/fv0arCM7O5uSkhJWr14d3Ldo0SL8fj8jRoxo2Rs4hzXUfxpS96F2/N+wbhhcndzcXLKzsxutIzs7m08//RSPxxNyTu/evUlMTDyt9t5yyy2sW7cuGCivWbOG9PR07r33Xv7zn/8Er9PUvjJ06FDsdnvIPW3ZsoW9e/ee9J6kvob61D//+U/Wrl1b7weOZcuWMXPmTCB8faoh+/fvp7i4mE6dOgWvoz7V9pzu+9eaNWtITEzE6XQCbauvibSKMCd7Sxu2bt0602az1Zuq8ESNzdp0vCeffDJkdqYTt0/Hjh07zMcee8xctWqVuWfPHvPzzz83r776ajMpKcksKioyTfPY9K/jx48316xZYy5YsMDs2LFjyPSvy5cvN3v37m3u378/uG/ixInm4MGDzeXLl5ufffaZmZWVpelfz1JD/eeDDz4wX375ZfObb74xd+3aZc6fP9/s27eveemllwbL1E2feO+995qbNm0y58yZU2/6xGeffdYcO3ZscLukpMRMTU01b7nlFnP9+vXmvHnzzKioqLOePvHEWVBM89R9Zf/+/Wbv3r3N5cuXB/fdddddZteuXc1FixaZq1atMrOzs83s7Ox619NMKSd3Ou9JixcvbnT619buU+Xl5eZ//dd/mXl5eeauXbvMTz75xBwyZIiZlZVl1tTUBMupT7U9DfW1f//73+aLL75ofvPNN+a2bdvM559/3oyKijIffPDBYJlwvn81NGvTiTNMxcfHh8zOdOJ2HfUbOV0KJOSkhg8fbr7wwgsnLdNcgcQrr7xiniy2zc/PNydNmmSmpKSYdrvd7NKli3nTTTeZmzdvDim3e/duc9KkSWZkZKSZnJxs/vznPzc9Hk/weN0XjV27dgX3FRcXm9OmTTNjYmLMuLg48/bbbzfLy8vrtUGBRNOc2H8WLVpkZmdnm/Hx8WZERISZlZVl/uIXvwj50meagb/RoEGDTIfDYfbs2bPeB91DDz1Ur/+sXbvWvOyyy0yn02l27tzZ/O1vf1uvzhP/7qfSUCBxqr5SN63i4sWLg/uqq6vNH/3oR2ZiYqIZFRVlfuc73zELCgrqXU8f3qd2qvekhgKJuv2t3aeqqqrM8ePHmx07djTtdrvZrVs384477giZnto01afaqhP72kcffWQOGjTIjImJMaOjo82BAweaL7zwgunz+ULOC9f7lwIJCQcFEnJSdb8Yn/hG2RIefPBBc9SoUS1+nbOhQKJpWrP/nMrLL79sZmZmmm63O9xNaZQ+vE9Nfapp1KfO3Pnc19Rv5HTZwjSiStqJyZMns23bNvLz84NrNbSUjz76iOeee65Fr3Gmli1bxqRJk3C5XEyePDnczWk3WrP/nMqHH37IY489Vi/5sC3Yu3cv/fr1w+12N5rLIwHqU6dHfersnY99Tf1GmsowzeNWUhGRBlVXV5Ofnw9ATEyMZkaRZuX1etm9ezcATqcz7F9apP1Tn5IzoX4jTaVAQkREREREmkzTv4qIiIiISJMpkBARERERkSZTICEiIiIiIk2mQEJERERERJpMgYSIiIiIiDSZAgkRkfNMXl4eVqu1wTVR3G43v//97xkyZAjR0dHEx8czcOBAHnjgAQ4cOBAsd9ttt2EYRr3XxIkTW/NWREQkjDT9q4jIeeYHP/gBMTExvPTSS2zZsoX09HQAXC4X48ePZ926dTz88MNceumldOzYkV27dvHGG2+QmJjI448/DgQCiaKiIl555ZWQup1OJ4mJia1+TyIi0vq0srWIyHmkoqKCN998k1WrVlFYWMjcuXP51a9+BcCTTz7JZ599xqpVqxg8eHDwnK5duzJq1ChO/N3J6XRqcUYRkfOYhjaJiJxH3nrrLfr06UPv3r25+eabefnll4MBwhtvvMGVV14ZEkQczzCM1myqiIi0cQokRETOIy+99BI333wzABMnTqS0tJSlS5cCsHXrVnr37h1S/jvf+Q4xMTHExMQwcuTIkGPz588PHqt7PfbYY61zIyIiEnYa2iQicp7YsmULK1as4N133wXAZrNxww038NJLLzF69OgGz3n++eeprKzkmWee4dNPPw05NmbMGP785z+H7EtKSmqRtouISNujQEJE5Dzx0ksv4fV6g8nVAKZp4nQ6ee6558jKymLLli0h53Tq1AloOECIjo4mMzOzZRstIiJtloY2iYicB7xeL6+++ip//OMfWbNmTfC1du1a0tPTeeONN5g2bRq5ubl8/fXX4W6uiIi0A3oiISJyHpg/fz5Hjx5lxowZxMfHhxybOnUqL730EsuWLeODDz5g3LhxPPTQQ1x++eUkJiaydetWPvroI6xWa8h5LpeLwsLCkH02m43k5OQWvx8REQk/rSMhInIeuPrqq/H7/XzwwQf1jq1YsYIRI0awdu1aevfuzVNPPcUbb7zB1q1b8fv99OjRg0mTJnHPPfeQkZEBBNaR+Pvf/16vrt69e7N58+YWvx8REQk/BRIiIiIiItJkypEQEREREZEmUyAhIiIiIiJNpkBCRERERESaTIGEiIiIiIg0mQIJERERERFpMgUSIiIiIiLSZAokRERERESkyRRIiIiIiIhIkymQEBERERGRJlMgISIiIiIiTaZAQkREREREmkyBhIiIiIiINNn/BwnEIgGiykeuAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 800x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 根据指定切分点计算WoE,并展示图形\n",
    "plot_woe(data,'AGE',bins=[-inf,30,40,50,inf])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "1c272f3a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>No.</th>\n",
       "      <th>Bin</th>\n",
       "      <th>#Total</th>\n",
       "      <th>#Bad</th>\n",
       "      <th>#Good</th>\n",
       "      <th>%Total</th>\n",
       "      <th>%Bad</th>\n",
       "      <th>%Good</th>\n",
       "      <th>%BadRate</th>\n",
       "      <th>WoE</th>\n",
       "      <th>IV</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AGE</td>\n",
       "      <td>1</td>\n",
       "      <td>(-inf, 30.0]</td>\n",
       "      <td>11013</td>\n",
       "      <td>2471</td>\n",
       "      <td>8542</td>\n",
       "      <td>36.7100%</td>\n",
       "      <td>37.2363%</td>\n",
       "      <td>36.5605%</td>\n",
       "      <td>22.4371%</td>\n",
       "      <td>0.0183</td>\n",
       "      <td>0.0001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AGE</td>\n",
       "      <td>2</td>\n",
       "      <td>(30.0, 40.0]</td>\n",
       "      <td>10713</td>\n",
       "      <td>2189</td>\n",
       "      <td>8524</td>\n",
       "      <td>35.7100%</td>\n",
       "      <td>32.9867%</td>\n",
       "      <td>36.4835%</td>\n",
       "      <td>20.4331%</td>\n",
       "      <td>-0.1008</td>\n",
       "      <td>0.0035</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AGE</td>\n",
       "      <td>3</td>\n",
       "      <td>(40.0, 50.0]</td>\n",
       "      <td>6005</td>\n",
       "      <td>1399</td>\n",
       "      <td>4606</td>\n",
       "      <td>20.0167%</td>\n",
       "      <td>21.0820%</td>\n",
       "      <td>19.7141%</td>\n",
       "      <td>23.2973%</td>\n",
       "      <td>0.0671</td>\n",
       "      <td>0.0009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>AGE</td>\n",
       "      <td>4</td>\n",
       "      <td>(50.0, inf]</td>\n",
       "      <td>2269</td>\n",
       "      <td>577</td>\n",
       "      <td>1692</td>\n",
       "      <td>7.5633%</td>\n",
       "      <td>8.6950%</td>\n",
       "      <td>7.2419%</td>\n",
       "      <td>25.4297%</td>\n",
       "      <td>0.1829</td>\n",
       "      <td>0.0027</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Total</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>30000</td>\n",
       "      <td>6636</td>\n",
       "      <td>23364</td>\n",
       "      <td>100.0000%</td>\n",
       "      <td>100.0000%</td>\n",
       "      <td>100.0000%</td>\n",
       "      <td>22.1200%</td>\n",
       "      <td>0.1675</td>\n",
       "      <td>0.0072</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Name No.           Bin  #Total  #Bad  #Good     %Total       %Bad  \\\n",
       "0    AGE   1  (-inf, 30.0]   11013  2471   8542   36.7100%   37.2363%   \n",
       "1    AGE   2  (30.0, 40.0]   10713  2189   8524   35.7100%   32.9867%   \n",
       "2    AGE   3  (40.0, 50.0]    6005  1399   4606   20.0167%   21.0820%   \n",
       "3    AGE   4   (50.0, inf]    2269   577   1692    7.5633%    8.6950%   \n",
       "4  Total                     30000  6636  23364  100.0000%  100.0000%   \n",
       "\n",
       "       %Good  %BadRate      WoE      IV  \n",
       "0   36.5605%  22.4371%   0.0183  0.0001  \n",
       "1   36.4835%  20.4331%  -0.1008  0.0035  \n",
       "2   19.7141%  23.2973%   0.0671  0.0009  \n",
       "3    7.2419%  25.4297%   0.1829  0.0027  \n",
       "4  100.0000%  22.1200%   0.1675  0.0072  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "woe_iv(data,'AGE',bins=[-inf,30,40,50,inf])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "e4b10e2b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 根据等频分箱，将特征分为5箱，并计算WoE,并展示图形\n",
    "plot_woe(data,'AGE',qcut=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "445b19a0",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "ename": "IndexError",
     "evalue": "list index out of range",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mIndexError\u001b[0m                                Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[21], line 2\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[38;5;66;03m# 根据默认的决策树方法分箱，并计算WoE,设置图形的颜色和样式，并展示图形\u001b[39;00m\n\u001b[1;32m----> 2\u001b[0m \u001b[43mplot_woe\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdata\u001b[49m\u001b[43m,\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mAGE\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43mcolor\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mblue\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43morange\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mred\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mblack\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43mlinewidth\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m2\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43mlinestyle\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43m--\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\n",
      "File \u001b[1;32m~\\miniconda3\\envs\\test\\lib\\site-packages\\westat\\model\\plot_woe.py:126\u001b[0m, in \u001b[0;36mplot_woe\u001b[1;34m(data, col, bins, qcut, missing, max_bins, target, method, trend, show_missing, precision, language, color, theme, marker, linewidth, linestyle, style, figsize, return_data)\u001b[0m\n\u001b[0;32m    124\u001b[0m \u001b[38;5;66;03m# 设置好坏客户数字标签\u001b[39;00m\n\u001b[0;32m    125\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m a, b, c \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mzip\u001b[39m(x, good, bad):\n\u001b[1;32m--> 126\u001b[0m     ax1\u001b[38;5;241m.\u001b[39mtext(a, b \u001b[38;5;241m/\u001b[39m \u001b[38;5;241m2\u001b[39m, b, ha\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mcenter\u001b[39m\u001b[38;5;124m'\u001b[39m, va\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mcenter\u001b[39m\u001b[38;5;124m'\u001b[39m, fontsize\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m12\u001b[39m, color\u001b[38;5;241m=\u001b[39m\u001b[43mcolor\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m4\u001b[39;49m\u001b[43m]\u001b[49m)\n\u001b[0;32m    127\u001b[0m     ax1\u001b[38;5;241m.\u001b[39mtext(a, b \u001b[38;5;241m+\u001b[39m c \u001b[38;5;241m/\u001b[39m \u001b[38;5;241m2\u001b[39m, c, ha\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mcenter\u001b[39m\u001b[38;5;124m'\u001b[39m, va\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mcenter\u001b[39m\u001b[38;5;124m'\u001b[39m, fontsize\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m12\u001b[39m, color\u001b[38;5;241m=\u001b[39mcolor[\u001b[38;5;241m4\u001b[39m])\n\u001b[0;32m    129\u001b[0m \u001b[38;5;66;03m# 设置woe数字标签\u001b[39;00m\n",
      "\u001b[1;31mIndexError\u001b[0m: list index out of range"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 根据默认的决策树方法分箱，并计算WoE,设置图形的颜色和样式，并展示图形\n",
    "plot_woe(data,'AGE',color=['blue','orange','red','black'],linewidth=2,linestyle='--')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3ee16c29",
   "metadata": {},
   "source": [
    "## 计算 IV 并绘图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c5552f95",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# 根据默认的决策树方法分箱，并计算IV，展示图形\n",
    "plot_iv(data,'PAY_0')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "93d9693f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 根据默认的决策树方法分箱，并计算IV，展示图形 和 数据\n",
    "plot_iv(data,'PAY_0',return_data=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fa6c47fc",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 根据决策树方法分箱，并计算IV，展示图形\n",
    "plot_iv(data,'PAY_0',method='tree')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "53e679e4",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 根据等频分箱，将特征分为5箱，并计算IV,展示图形\n",
    "plot_iv(data,'PAY_0',qcut=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8411e0f8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 根据指定的切分点，将特征分箱，并计算IV,展示图形\n",
    "plot_iv(data,'PAY_0',bins=[-inf,1,inf])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4afa4ffa",
   "metadata": {},
   "source": [
    "### 获取列的数据类型，离散型、连续型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a9f31517",
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "col_types = get_data_type(data)\n",
    "col_types"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "249b7115",
   "metadata": {},
   "source": [
    "## 决策树分箱"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "04e24420",
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "# 根据决策树方法，获取特征的分箱切分点结果\n",
    "tree_bins(data, 'AGE', max_leaf_nodes=4,min_samples_leaf=0.05)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b94d9518",
   "metadata": {},
   "source": [
    "## 批量决策树分箱"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "91e20b18",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "data_bins = get_data_bins(data,max_leaf_nodes=4)\n",
    "data_bins"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9c5b7839",
   "metadata": {},
   "source": [
    "## 手动调整分箱"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d1f4f93c",
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "data_bins_adjust = update_bins(data_bins,\n",
    "[\n",
    "['AGE',[-inf,20,30,40,50,inf]],\n",
    "['BILL_AMT1',[-inf,1000,8000,50000,inf]],\n",
    "])\n",
    "data_bins_adjust"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fb98447c",
   "metadata": {},
   "source": [
    "## 数据离散化"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b5f7cad6",
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "# 根据决策树分箱，并将数据离散化\n",
    "data_discrete(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7b8385c6",
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "# 按照调整后的分箱结果进行离散化\n",
    "data_train_discrete = data_discrete(data_train,col_bin = data_bins_adjust)\n",
    "data_train_discrete.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1c3ea15a",
   "metadata": {},
   "source": [
    "## WoE 转换"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5ec105e2",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# 使用已经离散化的数据，批量进行WoE转换\n",
    "data_train_woe = woe_transform(data_train_discrete)\n",
    "data_train_woe.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fc4bc09b",
   "metadata": {},
   "outputs": [],
   "source": [
    "## 指定 criterion='discrete'，使用已经离散化的列计算WoE和IV\n",
    "woe_iv(data_train_discrete, 'AGE',method='discrete')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5d2aafca",
   "metadata": {},
   "source": [
    "## 批量计算全部特征的IV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "33dcdf52",
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "# 使用默认的决策树分箱方法，批量计算数据集所有变量的IV\n",
    "df_iv = data_iv(data_train)  \n",
    "df_iv"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b52ed676",
   "metadata": {},
   "source": [
    "## 根据离散化的数据，批量计算IV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5e24c825",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 根据离散化的数据，批量计算IV\n",
    "df_iv = data_iv(data_train_discrete,method='discrete',precision=4)  \n",
    "df_iv"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b3dc89f0",
   "metadata": {},
   "source": [
    "## 通过最小IV >= 0.02 和最大相关性 <=0.6 筛选特征"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "463f4341",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# 通过最小IV >= 0.02 和最大相关性 <=0.6 筛选特征\n",
    "# 只返回筛选后保留的特征\n",
    "iv_corr(data_train,df_iv,min_iv = 0.02,max_corr = 0.6)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e0bdafda",
   "metadata": {},
   "source": [
    "## 通过最小IV >= 0.1 筛选特征"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5476668d",
   "metadata": {},
   "outputs": [],
   "source": [
    "iv_corr(data_train,df_iv,min_iv = 0.1,max_corr=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e265d933",
   "metadata": {},
   "source": [
    "## 通过最小IV >= 0.02 和最大相关性 <=0.6 筛选特征，并返回删除的特征"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "377e2371",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 返回筛选后保留的特征、根据IV删除的特征、根据corr删除的特征、相关矩阵\n",
    "col_keep,col_drop_by_iv,col_drop_by_corr,iv_corr_result = iv_corr(data_train,df_iv,min_iv = 0.02,max_corr=0.5,return_drop=True)\n",
    "col_keep"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f9e432f2",
   "metadata": {},
   "outputs": [],
   "source": [
    "#根据IV删除的特征\n",
    "col_drop_by_iv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "38c6723c",
   "metadata": {},
   "outputs": [],
   "source": [
    "#根据corr删除的特征\n",
    "col_drop_by_corr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4ea2055b",
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "# IV 和相关性矩阵\n",
    "iv_corr_result.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aaea3e43",
   "metadata": {},
   "source": [
    "## 逐步回归筛选特征"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a2e60d9d",
   "metadata": {},
   "outputs": [],
   "source": [
    "#逐步回归选择特征\n",
    "col_result = stepwise_lr(data_train)\n",
    "col_result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9673ed96",
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "#逐步回归选择特征，并显示逐步回归详情\n",
    "col_result = stepwise_lr(data_train,verbose=True)\n",
    "col_result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b054ab58",
   "metadata": {},
   "outputs": [],
   "source": [
    "import statsmodels.api as sm\n",
    "\n",
    "y = data_train_woe['y']\n",
    "x = data_train_woe[col_result]\n",
    "x = sm.add_constant(x)\n",
    "\n",
    "lr = sm.Logit(y, x).fit(disp=0)\n",
    "lr.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cadc39cd",
   "metadata": {},
   "source": [
    "## 查看特征相关性图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "766df073",
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "plot_corr(data_train[col_keep])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9fefd965",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 批量计算WoE，使用默认的决策树分箱方法\n",
    "data_iv(data,precision=4)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "71e2ffe3",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# 批量计算IV，等频分箱，分为5箱\n",
    "data_iv(data,qcut=5)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1deff19c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 根据分箱后的数据，批量计算iv\n",
    "data_iv(data_train_discrete)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6d911021",
   "metadata": {},
   "source": [
    "## 评分卡开发"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3bcb3081",
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "# 按照默认参数，开发评分卡\n",
    "# 默认基础分为600，PDO=50\n",
    "\n",
    "# 输入训练集数据 和 调整后分箱，开发评分卡\n",
    "scorecard = get_scorecard(data_train[col_keep + ['y']] ,data_bins_adjust)\n",
    "scorecard"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b9514e43",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 设置数据精度，并返回逻辑回归模型的详细内容\n",
    "scorecard,lr,a,b = get_scorecard(data_train[col_keep + ['y']] ,data_bins_adjust,precision=4,return_lr=True)\n",
    "\n",
    "# 导出评分卡为 excel\n",
    "scorecard.to_excel('scorecard.xlsx')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "908c5aa8",
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "# 查看逻辑回归模型的详细内容\n",
    "lr.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7b752b11",
   "metadata": {},
   "source": [
    "## 获取模型变量的IV表单"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "72be0dd1",
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "#获取模型变量的IV表单\n",
    "model_iv(data_train_discrete[col_keep+['y']])  "
   ]
  }
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